Cancer biomarkers

ABSTRACT

Biomarkers and methods using the biomarkers for the prediction of the recurrence risk of cancer in a patient are provided.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is the U.S. national phase entry of International Application no. PCT/US2010/020397 filed Jan. 7, 2010 (publication no. WO/2010/080933A1) and further claims the priority benefit of U.S. Provisional Application Ser. Nos. 61/143,077 (filed Jan. 7, 2009), 61/179,650 (filed May 19, 2009), 61/185,901 (filed Jun. 10, 2009), 61/241,748 (filed Sep. 11, 2009), 61/256,443 (filed Oct. 30, 2009), which are each hereby incorporated by reference in their entirety.

FIELD OF THE INVENTION

The invention generally relates to a molecular classification of disease and particularly to molecular markers for cancer and methods of use thereof.

BACKGROUND OF THE INVENTION

Cancer is a major public health problem, accounting for roughly 25% of all deaths in the United States. Though many treatments have been devised for various cancers, these treatments often vary in severity of side effects. It is useful for clinicians to know how aggressive a patient's cancer is in order to determine how aggressively to treat the cancer.

For example, most patients with early-stage asymptomatic prostate cancer are treated with radical prostatectomy or radiotherapy and optionally adjuvant therapy (e.g., hormone or chemotherapy), all of which have severe side effects. For many of these patients, however, these treatments and their associated side effects and costs are unnecessary because the cancer in these patients is not aggressive (i.e., grows slowly and is unlikely to cause mortality or significant morbidity during the patient's lifetime). In other patients the cancer is virulent (i.e., more likely to recur) and aggressive treatment is necessary to save the patient's life.

Some tools have been devised to help physicians in deciding which patients need aggressive treatment and which do not. In fact, several clinical parameters are currently in use for this purpose in various different cancers. In prostate cancer, for example, such clinical parameters include serum prostate-specific antigen (PSA), Gleason grade, pathologic stage, and surgical margins. In recent years clinical parameters have been made more helpful through their incorporation into continuous multivariable postoperative nomograms that calculate a patient's probability of having cancer progression/recurrence. See, e.g., Kattan et al., J. CLIN. ONCOL. (1999) 17:1499-1507; Stephenson et al., J. CLIN. ONCOL. (2005) 23:7005-7012. Despite these advances, however, many patients are given improper cancer treatments and there is still a serious need for novel and improved tools for predicting cancer recurrence.

SUMMARY OF THE INVENTION

The present invention is in part based on the determination that genes whose expression closely tracks the cell cycle (“cell-cycle genes” or “CCGs”) are particularly powerful genes for classifying certain cancers. Indeed, the inventors have discovered that CCGs provide most if not all of the predictive power in many prognostic signatures. For example, CCGs panels are shown herein to be able to predict the aggressiveness of prostate, bladder, brain, breast and lung cancer.

Thus one aspect of the invention provides a method of classifying cancer comprising determining the status of a panel of genes comprising at least two CCGs, wherein an abnormal status indicates a particular cancer classification. In some embodiments the status to be determined is gene expression levels. In some embodiments abnormal status indicates the cancer is aggressive, which may include having a poor prognosis, an increased likelihood of recurrence or progression, etc. In some embodiments the invention provides a method of determining the prognosis of a patient's cancer comprising determining the expression level of a panel of genes comprising at least two CCGs, wherein elevated expression indicates an increased likelihood of recurrence or progression of the cancer or the need for aggressive treatment.

The number of CCGs assayed can vary depending on, e.g., technical constraints, cost considerations, the classification being made, the cancer being tested, the desired level of predictive power, etc. However, it has been determined that certain minimum numbers of CCGs provide robust and significantly improved predictions. Thus in some embodiments of the invention the status of a panel comprising at least 5 CCGs is determined. In some embodiments the panel comprises at least 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more CCGs. In some embodiments the panel comprises at least 10, 15, 20, or more CCGs.

In some embodiments CCGs comprise at least a certain proportion of the panel of genes. Thus in some embodiments the panel comprises at least 25%, 30%, 40%, preferably at least 50%, 60%, 70%, more preferably at least 75%, 80%, 85%, and most preferably 90%, 95%, 96%, 97%, 98%, or 99% CCGs.

In some embodiments the CCGs are chosen from the group consisting of the genes in Table 1 and Panels A through G. In some embodiments the panel comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 110, 120, or more of the genes listed in any of Table 1 or Panels A through G.

It has been discovered that CCGs are particularly predictive in certain cancers. For example, a panel of CCGs has been determined to be accurate in predicting recurrence in prostate cancer. Further, CCGs can determine prognosis in bladder, brain, breast and lung cancer. Thus the invention provides a method comprising determining the status of a panel of genes comprising at least two CCGs, wherein an abnormal status indicates a poor prognosis. In some embodiments the panel comprises at least 2 genes chosen from the group of genes in at least one of Panels A through G. In some embodiments the panel comprises at least 10 genes chosen from the group of genes in at least one of Panels A through G. In some embodiments the panel comprises at least 15 genes chosen from the group of genes in at least one of Panels A through G. In some embodiments the panel comprises all of the genes in at least one of Panels A through G. The invention also provides a method of determining the prognosis of bladder cancer, comprising determining the status of a panel of genes comprising at least two CCGs (e.g., at least two of the genes in any of Panels B, C, & F), wherein an abnormal status indicates a poor prognosis. The invention also provides a method of determining the prognosis of brain cancer, comprising determining the status of a panel of genes comprising at least two CCGs (e.g., at least two of the genes in any of Panels B, C, & F), wherein an abnormal status indicates a poor prognosis. The invention further provides a method of determining the prognosis of breast cancer, comprising determining the status of a panel of genes comprising at least two CCGs (e.g., at least two of the genes in any of Panels B, C, & F), wherein an abnormal status indicates a poor prognosis. The invention also provides a method of determining the prognosis of lung cancer, comprising determining the status of a panel of genes comprising at least two CCGs (e.g., at least two of the genes in any of Panels B, C, & F), wherein an abnormal status indicates a poor prognosis.

It has further been discovered that CCG status synergistically adds to clinical parameters (e.g., clinical nomograms) in prognosing cancer. In the case of prostate cancer, it has been discovered that a high level of gene expression of any one of the genes in any of Panels A through F is associated with an increased risk of cancer recurrence or progression in patients whose clinical nomogram score indicates a relatively low risk of recurrence or progression. See, e.g., Examples 3 & 5. In the case of brain cancer, it has been discovered that a high level of gene expression of any one of the genes in any of Panels B, C or F is associated with an increased risk of cancer recurrence or progression independent of tumor grade. In the case of lung cancer, it has been discovered that a high level of gene expression of any one of the genes in any of Panels B, C or F is associated with an increased risk of cancer recurrence or progression independent of tumor stage. In the case of breast cancer, it has been discovered that a high level of gene expression of any one of the genes in any of Panels B, C or F is associated with an increased risk of cancer recurrence or progression independent of Nottingham Prognostic Index (NPI) nomogram score. Because evaluating CCG expression levels can thus detect increased risk not detected using clinical parameters alone, the invention generally provides methods combining evaluating at least one clinical parameter with evaluating the status of at least one CCG.

Thus one aspect of the invention provides an in vitro diagnostic method comprising determining at least one clinical parameter for a cancer patient and determining the status of at least one CCG in a sample obtained from the patient. In some embodiments the status of a plurality of CCGs (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50 or more) is determined. In some embodiments abnormal status indicates an increased likelihood of recurrence or progression. In some embodiments the patient has prostate cancer. In some embodiments the patient has lung cancer. Often the clinical parameter is at least somewhat independently predictive of recurrence or progression and the addition of CCG status improves the predictive power. Exemplary CCGs include those in Panels A through G. In some embodiments more than one clinical parameter is assessed along with the expression level of at least one CCG. In some embodiments more than one clinical parameter is assessed and the combination of parameters, along with the expression level of at least one CCG, predicts the likelihood of cancer recurrence or progression.

Clinical parameters are often combined and incorporated into nomograms for predicting risk of cancer recurrence or progression. Thus another aspect of the invention provides an in vitro diagnostic method comprising determining a clinical nomogram score for a cancer patient and determining the status of at least one CCG in a sample obtained from the patient. Often clinical nomograms are designed such that a high score correlates with an increased risk of recurrence. Similarly an elevated CCG status (e.g., increased expression or activity) is correlated with increased risk. Thus, in some embodiments the invention provides a method of determining whether a cancer patient has an increased likelihood of recurrence or progression comprising determining a clinical nomogram score for the patient and determining the status of at least one CCG in a sample obtained from the patient, wherein a high nomogram score and/or an elevated CCG status indicate the patient has an increased likelihood of recurrence or progression. In some embodiments the cancer is prostate cancer. In some embodiments the cancer is breast cancer.

In one aspect the invention provides an in vitro diagnostic method comprising determining at least one clinical parameter (or a nomogram score) for a patient and determining the status of a panel of CCGs. The number of CCGs assayed can vary depending on, e.g., technical constraints, cost considerations, the classification being made, the cancer being tested, the desired level of predictive power, etc. As an example, the invention provides an in vitro diagnostic method comprising determining a nomogram score for a patient and determining the expression level of two or more of the genes listed in Table 1 or Panels A through G. Any combination of CCGs (including any of those listed in Table 1 or Panels A through G) can be used to practice the invention. In one embodiment all CCGs listed in Table 1 or Panels A through G are assayed.

It has also been discovered that PTEN status predicts aggressive prostate cancer. PTEN status adds to both clinical parameters and to CCGs. PTEN status combined with CCG status is a better predictor than a clinical prostate nomogram. Because evaluating PTEN status can detect increased risk not detected using clinical parameters (e.g., clinical nomogram) or CCG expression levels alone or in combination, some aspects of the invention relate to methods combining a clinical parameter or CCG expression level with an evaluation of the status of PTEN.

Thus one aspect of the invention provides an in vitro diagnostic method comprising determining PTEN status and determining the status of a panel of CCGs in a sample obtained from a patient (and optionally determining one or more clinical parameters for the patient, e.g., clinical nomogram). Different combinations of techniques can be used to determine the status the various markers. For example, in one embodiment PTEN status is determined by immunohistochemistry (IHC) while the status of the plurality of CCGs is determined by quantitative polymerase chain reaction (qPCR™), e.g., TaqMan™.

Because PTEN and CCG status have been shown to independently correlate with risk of prostate cancer recurrence or progression, some embodiments provide a method of determining a prostate cancer patient's likelihood of recurrence comprising determining PTEN status in a sample obtained from the patient, wherein low or negative PTEN status indicates the patient has an increased likelihood of recurrence. Because PTEN and CCG status are also additive in predicting risk of prostate cancer recurrence or progression, some embodiments provide a method of determining a prostate cancer patient's likelihood of recurrence comprising determining PTEN status in a sample obtained from the patient and determining the status of a plurality of CCGs in a sample obtained from the patient, wherein low or negative PTEN status and/or elevated CCG status indicate the patient has an increased likelihood of recurrence.

PTEN status can add predictive value to clinical parameters in predicting prostate recurrence. Thus another aspect of the invention provides an in vitro diagnostic method comprising determining PTEN status and determining at least one clinical parameter for a prostate cancer patient. Often the clinical parameter is at least somewhat independently predictive of recurrence and the addition of PTEN status improves the predictive power. In some embodiments the invention provides a method of determining whether a cancer patient has an increased likelihood of recurrence comprising determining the status of PTEN in a sample obtained from the patient and determining a clinical nomogram score for the patient, wherein low or negative PTEN status and a high nomogram score indicate the patient has an increased likelihood of recurrence. In some embodiments the invention provides a method of determining whether a cancer patient has an increased likelihood of recurrence comprising determining the status of PTEN in a sample obtained from the patient, determining a clinical nomogram score for the patient and determining the status of at least one CCG in a sample obtained from the patient, wherein low or negative PTEN status, a high nomogram score and an elevated CCG status indicate the patient has an increased likelihood of recurrence.

It has been shown in the Examples below that all three markers may be combined to yield even greater predictive power. Thus some embodiments provide an in vitro method comprising determining PTEN status, determining the status of at least one CCG, and determining at least one clinical parameter.

The status of a gene includes any physical, chemical, or genetic characteristic of that gene. Examples include expression level, activity level, mutations, copy number, etc. In the context of CCGs for predicting risk of recurrence or progression, expression level and activity level are often the most helpful characterstics to assess in determining a CCG's status. In some embodiments, determining the expression level of a gene (e.g., a CCG or PTEN) comprises at least one of:

-   -   (a) determining the amount of polypeptide encoded by the gene;     -   (b) determining the amount of RNA encoded by the gene; or     -   (c) determining the expression and/or activity level of any         miRNA that regulates the gene.         In some embodiments, determining activity level may comprise,         e.g., determining the level of enzymatic activity of a         polypeptide encoded by a gene; determining the level of         interaction between a polypeptide and a binding partner; etc.

In one aspect, the invention provides compositions, microarrays and kits for use in the above methods. Such compositions include, but are not limited to, nucleic acid probes hybridizing to PTEN or a CCG (or to any nucleic acids encoded thereby or complementary thereto); nucleic acid primers and primer pairs suitable for amplifying all or a portion of PTEN or a CCG or any nucleic acids encoded thereby; antibodies binding immunologically to a polypeptide encoded by PTEN or a CCG; probe sets comprising a plurality of said nucleic acid probes, nucleic acid primers, antibodies, and/or polypeptides; microarrays comprising any of these; kits comprising any of these; etc. In some aspects, the invention provides computer methods, systems, software and/or modules for use in the above methods.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

Other features and advantages of the invention will be apparent from the following Detailed Description, and from the Claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of the predictive power over nomogram for CCG panels of different sizes.

FIG. 2 is an illustration of CCGs predicting time to recurrence.

FIG. 3 is an illustration of nomogram predicting time to recurrence.

FIG. 4 is an illustration of the non-overlapping recurrence predicted by nomogram and a CCG signature.

FIG. 5 is an illustration of time to recurrence for several patient populations defined by nomogram and/or CCG status.

FIG. 6 is an illustration of an example of a system useful in certain aspects and embodiments of the invention.

FIG. 7 is a flowchart illustrating an example of a computer-implemented method of the invention.

FIG. 8 a scatter plot comparing clinical parameters and CCG score as predictors of recurrence from Example 5.

FIG. 9 illustrates, from Example 5, the CCG threshold derived from analysis of the training cohort to the validation data set, with the CCG signature score effectively subdividing patients identified as low-risk using clinical parameters into patients with very low recurrence rates and a higher risk of recurrence.

FIG. 10 illustrates the predicted recurrence rate versus CCG score for patients in the validation cohort of Example 5.

FIG. 11 illustrates the predicted recurrence rate versus CCG score for patients in the validation cohort of Example 5.

FIG. 12 illustrates the distribution of clinical risk score in 443 patients studied in Example 5. The dark vertical line represents the threshold chosen by KM means to divide low- and high-risk patients and used throughout this study.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is based in part on the discovery that genes whose expression closely tracks the cell cycle (“cell-cycle genes” or “CCGs”) are particularly powerful genes for classifying certain cancers.

“Cell-cycle gene” and “CCG” herein refer to a gene whose expression level closely tracks the progression of the cell through the cell-cycle. See, e.g., Whitfield et al., MOL. BIOL. CELL (2002) 13:1977-2000. More specifically, CCGs show periodic increases and decreases in expression that coincide with certain phases of the cell cycle—e.g., STK15 and PLK show peak expression at G2/M. Id. Often CCGs have clear, recognized cell-cycle related function—e.g., in DNA synthesis or repair, in chromosome condensation, in cell-division, etc. However, some CCGs have expression levels that track the cell-cycle without having an obvious, direct role in the cell-cycle—e.g., UBE2S encodes a ubiquitin-conjugating enzyme, yet its expression closely tracks the cell-cycle. Thus a CCG according to the present invention need not have a recognized role in the cell-cycle. Exemplary CCGs are listed in Tables 1, 2, 3, and 4.

Whether a particular gene is a CCG may be determined by any technique known in the art, including that taught in Whitfield et al., MOL. BIOL. CELL (2002) 13:1977-2000. For example, a sample of cells, e.g., HeLa cells, can be synchronized such that they all progress through the different phases of the cell cycle at the same time. Generally this is done by arresting the cells in each phase—e.g., cells may be arrested in S phase by using a double thymidine block or in mitosis with a thymidine-nocodazole block. See, e.g., Whitfield et al., MOL. CELL. BIOL. (2000) 20:4188-4198. RNA is extracted from the cells after arrest in each phase and gene expression is quantitated using any suitable technique—e.g., expression microarray (genome-wide or specific genes of interest), real-time quantitative PCR™ (RTQ-PCR). Finally, statistical analysis (e.g., Fourier Transform) is applied to determine which genes show peak expression during particular cell-cycle phases. Genes may be ranked according to a periodicity score describing how closely the gene's expression tracks the cell-cycle—e.g., a high score indicates a gene very closely tracks the cell cycle. Finally, those genes whose periodicity score exceeds a defined threshold level (see Whitfield et al., MOL. BIOL. CELL (2002) 13:1977-2000) may be designated CCGs. A large, but not exhaustive, list of nucleic acids associated with CCGs (e.g., genes, ESTs, cDNA clones, etc.) is given in Table 1. See Whitfield et al., MOL. BIOL. CELL (2002) 13:1977-2000. All of the CCGs in Table 2 below form a panel of CCGs (“Panel A”) useful in the methods of the invention.

TABLE 2 Entrez RefSeq Accession Gene Symbol GeneID ABI Assay ID Nos. APOBEC3B* 9582 Hs00358981_m1 NM_004900.3 ASF1B* 55723 Hs00216780_m1 NM_018154.2 ASPM* 259266 Hs00411505_m1 NM_018136.4 ATAD2* 29028 Hs00204205_m1 NM_014109.3 BIRC5* 332 Hs00153353_m1; NM_001012271.1; Hs03043576_m1 NM_001012270.1; NM_001168.2 BLM* 641 Hs00172060_m1 NM_000057.2 BUB1 699 Hs00177821_m1 NM_004336.3 BUB1B* 701 Hs01084828_m1 NM_001211.5 C12orf48* 55010 Hs00215575_m1 NM_017915.2 C18orf24* 220134 Hs00536843_m1 NM_145060.3; NM_001039535.2 C1orf135* 79000 Hs00225211_m1 NM_024037.1 C21orf45* 54069 Hs00219050_m1 NM_018944.2 CCDC99* 54908 Hs00215019_m1 NM_017785.4 CCNA2* 890 Hs00153138_m1 NM_001237.3 CCNB1* 891 Hs00259126_m1 NM_031966.2 CCNB2* 9133 Hs00270424_m1 NM_004701.2 CCNE1* 898 Hs01026536_m1 NM_001238.1; NM_057182.1 CDC2* 983 Hs00364293_m1 NM_033379.3; NM_001130829.1; NM_001786.3 CDC20* 991 Hs03004916_g1 NM_001255.2 CDC45L* 8318 Hs00185895_m1 NM_003504.3 CDC6* 990 Hs00154374_m1 NM_001254.3 CDCA3* 83461 Hs00229905_m1 NM_031299.4 CDCA8* 55143 Hs00983655_m1 NM_018101.2 CDKN3* 1033 Hs00193192_m1 NM_001130851.1; NM_005192.3 CDT1* 81620 Hs00368864_m1 NM_030928.3 CENPA 1058 Hs00156455_m1 NM_001042426.1; NM_001809.3 CENPE* 1062 Hs00156507_m1 NM_001813.2 CENPF* 1063 Hs00193201_m1 NM_016343.3 CENPI* 2491 Hs00198791_m1 NM_006733.2 CENPM* 79019 Hs00608780_m1 NM_024053.3 CENPN* 55839 Hs00218401_m1 NM_018455.4; NM_001100624.1; NM_001100625.1 CEP55* 55165 Hs00216688_m1 NM_018131.4; NM_001127182.1 CHEK1* 1111 Hs00967506_m1 NM_001114121.1; NM_001114122.1; NM_001274.4 CKAP2* 26586 Hs00217068_m1 NM_018204.3; NM_001098525.1 CKS1B* 1163 Hs01029137_g1 NM_001826.2 CKS2* 1164 Hs01048812_g1 NM_001827.1 CTPS* 1503 Hs01041851_m1 NM_001905.2 CTSL2* 1515 Hs00952036_m1 NM_001333.2 DBF4* 10926 Hs00272696_m1 NM_006716.3 DDX39* 10212 Hs00271794_m1 NM_005804.2 DLGAP5/DLG7* 9787 Hs00207323_m1 NM_014750.3 DONSON* 29980 Hs00375083_m1 NM_017613.2 DSN1* 79980 Hs00227760_m1 NM_024918.2 DTL* 51514 Hs00978565_m1 NM_016448.2 E2F8* 79733 Hs00226635_m1 NM_024680.2 ECT2* 1894 Hs00216455_m1 NM_018098.4 ESPL1* 9700 Hs00202246_m1 NM_012291.4 EXO1* 9156 Hs00243513_m1 NM_130398.2; NM_003686.3; NM_006027.3 EZH2* 2146 Hs00544830_m1 NM_152998.1; NM_004456.3 FANCI* 55215 Hs00289551_m1 NM_018193.2; NM_001113378.1 FBXO5* 26271 Hs03070834_m1 NM_001142522.1; NM_012177.3 FOXM1* 2305 Hs01073586_m1 NM_202003.1; NM_202002.1; NM_021953.2 GINS1* 9837 Hs00221421_m1 NM_021067.3 GMPS* 8833 Hs00269500_m1 NM_003875.2 GPSM2* 29899 Hs00203271_m1 NM_013296.4 GTSE1* 51512 Hs00212681_m1 NM_016426.5 H2AFX* 3014 Hs00266783_s1 NM_002105.2 HMMR* 3161 Hs00234864_m1 NM_001142556.1; NM_001142557.1; NM_012484.2; NM_012485.2 HN1* 51155 Hs00602957_m1 NM_001002033.1; NM_001002032.1; NM_016185.2 KIAA0101* 9768 Hs00207134_m1 NM_014736.4 KIF11* 3832 Hs00189698_m1 NM_004523.3 KIF15* 56992 Hs00173349_m1 NM_020242.2 KIF18A* 81930 Hs01015428_m1 NM_031217.3 KIF20A* 10112 Hs00993573_m1 NM_005733.2 KIF20B/ 9585 Hs01027505_m1 NM_016195.2 MPHOSPH1* KIF23* 9493 Hs00370852_m1 NM_138555.1; NM_004856.4 KIF2C* 11004 Hs00199232_m1 NM_006845.3 KIF4A* 24137 Hs01020169_m1 NM_012310.3 KIFC1* 3833 Hs00954801_m1 NM_002263.3 KPNA2 3838 Hs00818252_g1 NM_002266.2 LMNB2* 84823 Hs00383326_m1 NM_032737.2 MAD2L1 4085 Hs01554513_g1 NM_002358.3 MCAM* 4162 Hs00174838_m1 NM_006500.2 MCM10* 55388 Hs00960349_m1 NM_018518.3; NM_182751.1 MCM2* 4171 Hs00170472_m1 NM_004526.2 MCM4* 4173 Hs00381539_m1 NM_005914.2; NM_182746.1 MCM6* 4175 Hs00195504_m1 NM_005915.4 MCM7* 4176 Hs01097212_m1 NM_005916.3; NM_182776.1 MELK 9833 Hs00207681_m1 NM_014791.2 MKI67* 4288 Hs00606991_m1 NM_002417.3 MYBL2* 4605 Hs00231158_m1 NM_002466.2 NCAPD2* 9918 Hs00274505_m1 NM_014865.3 NCAPG* 64151 Hs00254617_m1 NM_022346.3 NCAPG2* 54892 Hs00375141_m1 NM_017760.5 NCAPH* 23397 Hs01010752_m1 NM_015341.3 NDC80* 10403 Hs00196101_m1 NM_006101.2 NEK2* 4751 Hs00601227_mH NM_002497.2 NUSAP1* 51203 Hs01006195_m1 NM_018454.6; NM_001129897.1; NM_016359.3 OIP5* 11339 Hs00299079_m1 NM_007280.1 ORC6L* 23594 Hs00204876_m1 NM_014321.2 PAICS* 10606 Hs00272390_m1 NM_001079524.1; NM_001079525.1; NM_006452.3 PBK* 55872 Hs00218544_m1 NM_018492.2 PCNA* 5111 Hs00427214_g1 NM_182649.1; NM_002592.2 PDSS1* 23590 Hs00372008_m1 NM_014317.3 PLK1* 5347 Hs00153444_m1 NM_005030.3 PLK4* 10733 Hs00179514_m1 NM_014264.3 POLE2* 5427 Hs00160277_m1 NM_002692.2 PRC1* 9055 Hs00187740_m1 NM_199413.1; NM_199414.1; NM_003981.2 PSMA7* 5688 Hs00895424_m1 NM_002792.2 PSRC1* 84722 Hs00364137_m1 NM_032636.6; NM_001005290.2; NM_001032290.1; NM_001032291.1 PTTG1* 9232 Hs00851754_u1 NM_004219.2 RACGAP1* 29127 Hs00374747_m1 NM_013277.3 RAD51* 5888 Hs00153418_m1 NM_133487.2; NM_002875.3 RAD51AP1* 10635 Hs01548891_m1 NM_001130862.1; NM_006479.4 RAD54B* 25788 Hs00610716_m1 NM_012415.2 RAD54L* 8438 Hs00269177_m1 NM_001142548.1; NM_003579.3 RFC2* 5982 Hs00945948_m1 NM_181471.1; NM_002914.3 RFC4* 5984 Hs00427469_m1 NM_181573.2; NM_002916.3 RFC5* 5985 Hs00738859_m1 NM_181578.2; NM_001130112.1; NM_001130113.1; NM_007370.4 RNASEH2A* 10535 Hs00197370_m1 NM_006397.2 RRM2* 6241 Hs00357247_g1 NM_001034.2 SHCBP1* 79801 Hs00226915_m1 NM_024745.4 SMC2* 10592 Hs00197593_m1 NM_001042550.1; NM_001042551.1; NM_006444.2 SPAG5* 10615 Hs00197708_m1 NM_006461.3 SPC25* 57405 Hs00221100_m1 NM_020675.3 STIL* 6491 Hs00161700_m1 NM_001048166.1; NM_003035.2 STMN1* 3925 Hs00606370_m1; NM_005563.3; Hs01033129_m1 NM_203399.1 TACC3* 10460 Hs00170751_m1 NM_006342.1 TIMELESS* 8914 Hs01086966_m1 NM_003920.2 TK1* 7083 Hs01062125_m1 NM_003258.4 TOP2A* 7153 Hs00172214_m1 NM_001067.2 TPX2* 22974 Hs00201616_m1 NM_012112.4 TRIP13* 9319 Hs01020073_m1 NM_004237.2 TTK* 7272 Hs00177412_m1 NM_003318.3 TUBA1C* 84790 Hs00733770_m1 NM_032704.3 TYMS* 7298 Hs00426591_m1 NM_001071.2 UBE2C 11065 Hs00964100_g1 NM_181799.1; NM_181800.1; NM_181801.1; NM_181802.1; NM_181803.1; NM_007019.2 UBE2S 27338 Hs00819350_m1 NM_014501.2 VRK1* 7443 Hs00177470_m1 NM_003384.2 ZWILCH* 55055 Hs01555249_m1 NM_017975.3; NR_003105.1 ZWINT* 11130 Hs00199952_m1 NM_032997.2; NM_001005413.1; NM_007057.3 *124-gene subset of CCGs useful in the invention (“Panel B”). ABI Assay ID means the catalogue ID number for the gene expression assay commercially available from Applied Biosystems Inc. (Foster City, CA) for the particular gene.

Though not wishing to be bound by any theory, it is thought that tissue samples containing a relatively high concentration of rapidly proliferating cells will show, in the aggregate, relatively higher expression of CCGs. This is thought to be due to some CCGs being up-regulated during cell proliferation (i.e., during certain phases of the cell-cycle). In turn, tumors comprising a high concentration of rapidly proliferating cells tend to be more aggressive and patients having such tumors tend to have a worse prognosis. Indeed, the inventors have determined that CCGs provide most if not all of the predictive power in most prognostic cancer signatures. For example, panels having CCGs can predict the aggressiveness of prostate, brain, breast and lung cancer.

Thus one aspect of the invention provides a method of classifying cancer comprising determining the status of a panel of genes comprising at least two CCGs, in tissue or cell sample, particularly a tumor sample, from a patient, wherein an abnormal status indicates a negative cancer classification. As used herein, “determining the status” of a gene refers to determining the presence, absence, or extent/level of some physical, chemical, or genetic characteristic of the gene or its expression product(s). Such characteristics include, but are not limited to, expression levels, activity levels, mutations, copy number, methylation status, etc.

In the context of CCGs as used to determine risk of cancer recurrence or progression or need for aggressive treatment, particularly useful characteristics include expression levels (e.g., mRNA or protein levels) and activity levels. Characteristics may be assayed directly (e.g., by assaying a CCG's expression level) or determined indirectly (e.g., assaying the level of a gene or genes whose expression level is correlated to the expression level of the CCG). Thus some embodiments of the invention provide a method of classifying cancer comprising determining the expression level, particularly mRNA level of a panel of genes comprising at least two CCGs, in a tumor sample, wherein elevated expression indicates a negative cancer classification, or an increased risk of cancer recurrence or progression, or a need for aggressive treatment.

Those skilled in the art are familiar with various techniques for determining the status of a gene or protein in a tissue or cell sample including, but not limited to, microarray analysis (e.g., for assaying mRNA or microRNA expression, copy number, etc.), quantitative real-time PCR™ (“qRT-PCR™”, e.g., TaqMan™), immunoanalysis (e.g., ELISA, immunohistochemistry), etc. The activity level of a polypeptide encoded by a gene may be used in much the same way as the expression level of the gene or polypeptide. Often higher activity levels indicate higher expression levels and while lower activity levels indicate lower expression levels. Thus, in some embodiments, the invention provides any of the methods discussed above, wherein the activity level of a polypeptide encoded by the CCG is determined rather than or in addition to the expression level of the CCG. Those skilled in the art are familiar with techniques for measuring the activity of various such proteins, including those encoded by the genes listed in Tables 1, 2, 3, and 4. The methods of the invention may be practiced independent of the particular technique used.

“Abnormal status” means a marker's status in a particular sample differs from the status generally found in average samples (e.g., healthy samples or average diseased samples). Examples include mutated, elevated, decreased, present, absent, etc. An “elevated status” means that one or more of the above characteristics (e.g., expression or mRNA level) is higher than normal levels. Generally this means an increase in the characteristic (e.g., expression or mRNA level) as compared to an index value. Conversely a “low status” means that one or more of the above characteristics (e.g., gene expression or mRNA level) is lower than normal levels. Generally this means a decrease in the characteristic (e.g., expression) as compared to an index value. In this context, a “negative status” generally means the characteristic is absent or undetectable. For example, PTEN status is negative if PTEN nucleic acid and/or protein is absent or undetectable in a sample. However, negative PTEN status also includes a mutation or copy number reduction in PTEN.

Those skilled in the art will appreciate how to obtain and use an index value in the methods of the invention. For example, the index value may represent the gene expression levels found in a normal sample obtained from the patient of interest, in which case an expression level in the tumor sample significantly higher than this index value would indicate, e.g., a poor prognosis or increased likelihood of cancer recurrence or a need for aggressive treatment.

Alternatively, the index value may represent the average expression level of for a set of individuals from a diverse cancer population or a subset of the population. For example, one may determine the average expression level of a gene or gene panel in a random sampling of patients with cancer (e.g., prostate, bladder, brain, breast, or lung cancer). This average expression level may be termed the “threshold index value,” with patients having CCG expression higher than this value expected to have a poorer prognosis than those having expression lower than this value.

Alternatively the index value may represent the average expression level of a particular gene marker in a plurality of training patients (e.g., prostate cancer patients) with similar outcomes whose clinical and follow-up data are available and sufficient to define and categorize the patients by disease outcome, e.g., recurrence or prognosis. See, e.g., Examples, infra. For example, a “good prognosis index value” can be generated from a plurality of training cancer patients characterized as having “good outcome”, e.g., those who have not had cancer recurrence five years (or ten years or more) after initial treatment, or who have not had progression in their cancer five years (or ten years or more) after initial diagnosis. A “poor prognosis index value” can be generated from a plurality of training cancer patients defined as having “poor outcome”, e.g., those who have had cancer recurrence within five years (or ten years, etc.) after initial treatment, or who have had progression in their cancer within five years (or ten years, etc.) after initial diagnosis. Thus, a good prognosis index value of a particular gene may represent the average level of expression of the particular gene in patients having a “good outcome,” whereas a poor prognosis index value of a particular gene represents the average level of expression of the particular gene in patients having a “poor outcome.”

In some embodiments of the invention the methods comprise determining the expression of one or more CCGs and, if this expression is “increased,” the patient has a poor prognosis. In the context of the invention, “increased” expression of a CCG means the patient's expression level is either elevated over a normal index value or a threshold index (e.g., by at least some threshold amount) or closer to the “poor prognosis index value” than to the “good prognosis index value.”

Thus, when the determined level of expression of a relevant gene marker is closer to the good prognosis index value of the gene than to the poor prognosis index value of the gene, then it can be concluded that the patient is more likely to have a good prognosis, i.e., a low (or no increased) likelihood of cancer recurrence. On the other hand, if the determined level of expression of a relevant gene marker is closer to the poor prognosis index value of the gene than to the good prognosis index value of the gene, then it can be concluded that the patient is more likely to have a poor prognosis, i.e., an increased likelihood of cancer recurrence.

Alternatively index values may be determined thusly: In order to assign patients to risk groups, a threshold value will be set for the cell cycle mean. The optimal threshold value is selected based on the receiver operating characteristic (ROC) curve, which plots sensitivity vs (1−specificity). For each increment of the cell cycle mean, the sensitivity and specificity of the test is calculated using that value as a threshold. The actual threshold will be the value that optimizes these metrics according to the artisans requirements (e.g., what degree of sensitivity or specificity is desired, etc.). Example 5 demonstrates determination of a threshold value determined and validated experimentally.

In some embodiments the level of expression of a particular gene (particularly CCG) is obtained as the absolute number of copies of mRNA a particular tumor tissue sample or cell. In other embodiments, the level of expression of a particular gene (particularly CCG) is obtained by normalizing the amount of an expression product of a particular gene of interest against the amount of expression of a normalizing gene (e.g., one or more housekeeping genes) product. Normalization may be done to generate an index value or simply to help in reducing background noise when determining the expression level of the gene of interest. In one embodiment, for example, in determining the level of expression of a relevant gene in accordance with the present invention, the amount of an expression product of the gene (e.g., mRNA, cDNA, protein) is measured within one or more cells, particularly tumor cells, and normalized against the amount of the expression product(s) of a normalizing gene, or a set of normalizing genes, within the same one or more cells, to obtain the level of expression of the relevant marker gene. For example, when a single gene is used as a normalizing gene, a housekeeping gene whose expression is determined to be independent of prostate cancer outcome/prognosis or not to vary between normal and cancerous prostate cells can be used. A set of such housekeeping genes can also be used in gene expression analysis to provide a combined normalizing gene set. Housekeeping genes are well known in the art, with examples including, but are not limited to, GUSB (glucuronidase, beta), HMBS (hydroxymethylbilane synthase), SDHA (succinate dehydrogenase complex, subunit A, flavoprotein), UBC (ubiquitin C) and YWHAZ (tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide). When a combined normalizing gene set is used in the normalization, the amount of gene expression of such normalizing genes can be averaged, combined together by straight additions or by a defined algorithm. Genes other than housekeeping genes may also be used as normalizing genes. Other examples of particularly useful housekeeper genes for use in the methods and compositions of the invention include those listed in Table A below.

TABLE A Gene Entrez Applied Biosystems Symbol GeneID Assay ID RefSeq Accession Nos. CLTC* 1213 Hs00191535_m1 NM_004859.3 GUSB 2990 Hs99999908_m1 NM_000181.2 HMBS 3145 Hs00609297_m1 NM_000190.3 MMADHC* 27249 Hs00739517_g1 NM_015702.2 MRFAP1* 93621 Hs00738144_g1 NM_033296.1 PPP2CA* 5515 Hs00427259_m1 NM_002715.2 PSMA1* 5682 Hs00267631_m1 PSMC1* 5700 Hs02386942_g1 NM_002802.2 RPL13A* 23521 Hs03043885_g1 NM_012423.2 RPL37* 6167 Hs02340038_g1 NM_000997.4 RPL38* 6169 Hs00605263_g1 NM_000999.3 RPL4* 6124 Hs03044647_g1 NM_000968.2 RPL8* 6132 Hs00361285_g1 NM_033301.1; NM_000973.3 RPS29* 6235 Hs03004310_g1 NM_001030001.1; NM_001032.3 SDHA 6389 Hs00188166_m1 NM_004168.2 SLC25A3* 6515 Hs00358082_m1 NM_213611.1; NM_002635.2; NM_005888.2 TXNL1* 9352 Hs00355488_m1 NR_024546.1; NM_004786.2 UBA52* 7311 Hs03004332_g1 NM_001033930.1; NM_003333.3 UBC 7316 Hs00824723_m1 NM_021009.4 YWHAZ 7534 Hs00237047_m1 NM_003406.3 *Subset of housekeeping genes used in, e.g., Example 5.

Panels of CCGs (e.g., 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more CCGs) can accurately predict prognosis, as shown in Example 3. Those skilled in the art are familiar with various ways of determining the expression of a panel of genes (i.e., a plurality of genes). One may determine the expression of a panel of genes by determining the average expression level (normalized or absolute) of all panel genes in a sample obtained from a particular patient (either throughout the sample or in a subset of cells from the sample or in a single cell). Increased expression in this context will mean the average expression is higher than the average expression level of these genes in normal patients (or higher than some index value that has been determined to represent the average expression level in a reference population such as patients with the same cancer). Alternatively, one may determine the expression of a panel of genes by determining the average expression level (normalized or absolute) of at least a certain number (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30 or more) or at least a certain proportion (e.g., 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 100%) of the genes in the panel. Alternatively, one may determine the expression of a panel of genes by determining the absolute copy number of the mRNA (or protein) of all the genes in the panel and either total or average these across the genes.

As used herein, “classifying a cancer” and “cancer classification” refer to determining one or more clinically-relevant features of a cancer and/or determining a particular prognosis of a patient having said cancer. Thus “classifying a cancer” includes, but is not limited to: (i) evaluating metastatic potential, potential to metastasize to specific organs, risk of recurrence, and/or course of the tumor; (ii) evaluating tumor stage; (iii) determining patient prognosis in the absence of treatment of the cancer; (iv) determining prognosis of patient response (e.g., tumor shrinkage or progression-free survival) to treatment (e.g., chemotherapy, radiation therapy, surgery to excise tumor, etc.); (v) diagnosis of actual patient response to current and/or past treatment; (vi) determining a preferred course of treatment for the patient; (vii) prognosis for patient relapse after treatment (either treatment in general or some particular treatment); (viii) prognosis of patient life expectancy (e.g., prognosis for overall survival), etc.

Thus, a “negative classification” means an unfavorable clinical feature of the cancer (e.g., a poor prognosis). Examples include (i) an increased metastatic potential, potential to metastasize to specific organs, and/or risk of recurrence; (ii) an advanced tumor stage; (iii) a poor patient prognosis in the absence of treatment of the cancer; (iv) a poor prognosis of patient response (e.g., tumor shrinkage or progression-free survival) to a particular treatment (e.g., chemotherapy, radiation therapy, surgery to excise tumor, etc.); (v) a poor prognosis for patient relapse after treatment (either treatment in general or some particular treatment); (vi) a poor prognosis of patient life expectancy (e.g., prognosis for overall survival), etc. In some embodiments a recurrence-associated clinical parameter (or a high nomogram score) and increased expression of a CCG indicate a negative classification in cancer (e.g., increased likelihood of recurrence or progression).

As discussed above, it is thought that elevated CCG expression accompanies rapidly proliferating (and thus more aggressive) cancer cells. Such a cancer in a patient will often mean the patient has an increased likelihood of recurrence after treatment (e.g., the cancer cells not killed or removed by the treatment will quickly grow back). Such a cancer can also mean the patient has an increased likelihood of cancer progression for more rapid progression (e.g., the rapidly proliferating cells will cause any tumor to grow quickly, gain in virulence, and/or metastasize). Such a cancer can also mean the patient may require a relatively more aggressive treatment. Thus, in some embodiments the invention provides a method of classifying cancer comprising determining the status of a panel of genes comprising at least two CCGs, wherein an abnormal status indicates an increased likelihood of recurrence or progression. As discussed above, in some embodiments the status to be determined is gene expression levels. Thus in some embodiments the invention provides a method of determining the prognosis of a patient's cancer comprising determining the expression level of a panel of genes comprising at least two CCGs, wherein elevated expression indicates an increased likelihood of recurrence or progression of the cancer.

“Recurrence” and “progression” are terms well-known in the art and are used herein according to their known meanings. As an example, the meaning of “progression” may be cancer-type dependent, with progression in lung cancer meaning something different from progression in prostate cancer. However, within each cancer-type and subtype “progression” is clearly understood to those skilled in the art. As used herein, a patient has an “increased likelihood” of some clinical feature or outcome (e.g., recurrence or progression) if the probability of the patient having the feature or outcome exceeds some reference probability or value. The reference probability may be the probability of the feature or outcome across the general relevant patient population. For example, if the probability of recurrence in the general prostate cancer population is X % and a particular patient has been determined by the methods of the present invention to have a probability of recurrence of Y %, and if Y>X, then the patient has an “increased likelihood” of recurrence. Alternatively, as discussed above, a threshold or reference value may be determined and a particular patient's probability of recurrence may be compared to that threshold or reference. Because predicting recurrence and predicting progression are prognostic endeavors, “predicting prognosis” will often be used herein to refer to either or both. In these cases, a “poor prognosis” will generally refer to an increased likelihood of recurrence, progression, or both.

As shown in Example 3, individual CCGs can predict prognosis quite well. Thus the invention provides a method of predicting prognosis comprising determining the expression of at least one CCG listed in Table 1 or Panels A through G.

Example 3 also shows that panels of CCGs (e.g., 2, 3, 4, 5, or 6 CCGs) can accurately predict prognosis. Thus in some aspects the invention provides a method of classifying a cancer comprising determining the status of a panel (i.e., a plurality) of genes comprising a plurality of CCGs. For example, increased expression in a panel of genes may refer to the average expression level of all panel genes in a particular patient being higher than the average expression level of these genes in normal patients (or higher than some index value that has been determined to represent the normal average expression level). Alternatively, increased expression in a panel of genes may refer to increased expression in at least a certain number (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30 or more) or at least a certain proportion (e.g., 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 100%) of the genes in the panel as compared to the average normal expression level.

In some embodiments the panel comprises at least 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 70, 80, 90, 100, 200, or more CCGs. In some embodiments the panel comprises at least 10, 15, 20, or more CCGs. In some embodiments the panel comprises between 5 and 100 CCGs, between 7 and 40 CCGs, between 5 and 25 CCGs, between 10 and 20 CCGs, or between 10 and 15 CCGs. In some embodiments CCGs comprise at least a certain proportion of the panel. Thus in some embodiments the panel comprises at least 25%, 30%, 40%, 50%, 60%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% CCGs. In some preferred embodiments the panel comprises at least 10, 15, 20, 25, 30, 35, 40, 45, 50, 70, 80, 90, 100, 200, or more CCGs, and such CCGs constitute of at least 50%, 60%, 70%, preferably at least 75%, 80%, 85%, more preferably at least 90%, 95%, 96%, 97%, 98%, or 99% or more of the total number of genes in the panel. In some embodiments the CCGs are chosen from the group consisting of the genes in Table 1 and Panels A through G. In some embodiments the panel comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, 30, or more of the genes in any of Table 1 and Panels A through G. In some embodiments the invention provides a method of predicting prognosis comprising determining the status of the CCGs in Panels A through G, wherein abnormal status indicates a poor prognosis.

In some of these embodiments elevated expression indicates an increased likelihood of recurrence or progression. Thus in a preferred embodiment the invention provides a method of predicting risk of cancer recurrence or progression in a patient comprising determining the status of a panel of genes, wherein the panel comprises between about 10 and about 15 CCGs, the CCGs constitute at least 90% of the panel, and an elevated status for the CCGs indicates an increased likelihood or recurrence or progression.

Several panels of CCGs (Table 2, supra, and Tables 3 & 4, infra) have been evaluated for their ability to predict prognosis in several different cancers. The results of these studies are described in Examples 1 through 6 below.

TABLE 3 “Panel C” Evaluated in Examples 1 through 4 Gene Entrez Symbol GeneID AURKA 6790 BUB1* 699 CCNB1* 891 CCNB2* 9133 CDC2* 983 CDC20* 991 CDC45L* 8318 CDCA8* 55143 CENPA 1058 CKS2* 1164 DLG7* 9787 DTL* 51514 FOXM1* 2305 HMMR* 3161 KIF23* 9493 KPNA2 3838 MAD2L1* 4085 MELK 9833 MYBL2* 4605 NUSAP1* 51203 PBK* 55872 PRC1* 9055 PTTG1* 9232 RRM2* 6241 TIMELESS* 8914 TPX2* 22974 TRIP13* 9319 TTK* 7272 UBE2C 11065 UBE2S* 27338 ZWINT* 11130 *These genes were used as a 26-gene subset panel (“Panel D”) in the validation arm of the experiment described in Example 2.

TABLE 4 “Panel E” Name GeneID ASF1B* 55723 ASPM* 259266 BIRC5* 332 BUB1B* 701 C18orf24* 220134 CDC2* 983 CDC20* 991 CDCA3* 83461 CDCA8* 55143 CDKN3* 1033 CENPF* 1063 CENPM* 79019 CEP55* 55165 DLGAP5* 9787 DTL* 51514 FOXM1* 2305 KIAA0101* 9768 KIF11* 3832 KIF20A* 10112 KIF4A 24137 MCM10* 55388 NUSAP1* 51203 ORC6L* 23594 PBK* 55872 PLK1* 5347 PRC1* 9055 PTTG1* 9232 RAD51* 5888 RAD54L* 8438 RRM2* 6241 TK1* 7083 TOP2A* 7153 *These genes were used as a 31-gene subset panel (“Panel F”) in the experiment described in Example 5.

It has been determined that the choice of individual CCGs for a panel can often be relatively arbitrary. In other words, most CCGs have been found to be very good surrogates for each other. One way of assessing whether particular CCGs will serve well in the methods and compositions of the invention is by assessing their correlation with the mean expression of CCGs (e.g., all known CCGs, a specific set of CCGs, etc.). Those CCGs that correlate particularly well with the mean are expected to perform well in assays of the invention, e.g., because these will reduce noise in the assay. A ranking of select CCGs according to their correlation with the mean CCG expression is given in Table 23.

In CCG signatures the particular CCGs assayed is often not as important as the total number of CCGs. The number of CCGs assayed can vary depending on many factors, e.g., technical constraints, cost considerations, the classification being made, the cancer being tested, the desired level of predictive power, etc. Increasing the number of CCGs assayed in a panel according to the invention is, as a general matter, advantageous because, e.g., a larger pool of mRNAs to be assayed means less “noise” caused by outliers and less chance of an assay error throwing off the overall predictive power of the test. However, cost and other considerations will generally limit this number and finding the optimal number of CCGs for a signature is desirable.

It has been discovered that the predictive power of a CCG signature often ceases to increase significantly beyond a certain number of CCGs (see FIG. 1; Example 1). More specifically, the optimal number of CCGs in a signature (n_(O)) can be found wherever the following is true

(P _(n+1) −P _(n))<C _(O),

wherein P is the predictive power (i.e., P_(n) is the predictive power of a signature with n genes and P_(n+1) is the predictive power of a signature with n genes plus one) and C_(O) is some optimization constant. Predictive power can be defined in many ways known to those skilled in the art including, but not limited to, the signature's p-value. C_(O) can be chosen by the artisan based on his or her specific constraints. For example, if cost is not a critical factor and extremely high levels of sensitivity and specificity are desired, C_(O) can be set very low such that only trivial increases in predictive power are disregarded. On the other hand, if cost is decisive and moderate levels of sensitivity and specificity are acceptable, C_(O) can be set higher such that only significant increases in predictive power warrant increasing the number of genes in the signature.

Alternatively, a graph of predictive power as a function of gene number may be plotted (as in FIG. 1) and the second derivative of this plot taken. The point at which the second derivative decreases to some predetermined value (C_(O)') may be the optimal number of genes in the signature.

Examples 1 & 3 and FIG. 1 illustrate the empirical determination of optimal numbers of CCGs in CCG panels of the invention. Randomly selected subsets of the 31 CCGs listed in Table 3 were tested as distinct CCG signatures and predictive power (i.e., p-value) was determined for each. As FIG. 1 shows, p-values ceased to improve significantly between about 10 and about 15 CCGs, thus indicating that an optimal number of CCGs in a prognostic panel is from about 10 to about 15. Thus some embodiments of the invention provide a method of predicting prognosis in a patient having prostate cancer comprising determining the status of a panel of genes, wherein the panel comprises between about 10 and about 15 CCGs and an elevated status for the CCGs indicates a poor prognosis. In some embodiments the panel comprises between about 10 and about 15 CCGs and the CCGs constitute at least 90% of the panel. In other embodiments the panel comprises CCGs plus one or more additional markers that significantly increase the predictive power of the panel (i.e., make the predictive power significantly better than if the panel consisted of only the CCGs). Any other combination of CCGs (including any of those listed in Table 1 or Panels A through G) can be used to practice the invention.

It has been discovered that CCGs are particularly predictive in certain cancers. For example, panels of CCGs have been determined to be accurate in predicting recurrence in prostate cancer (Examples 1 through 5). Further, CCGs can determine prognosis in bladder, brain, breast and lung cancers, as summarized in Example 6 and Tables 21 and 22 below.

Thus the invention provides a method comprising determining the status of a panel of genes comprising at least two CCGs, wherein an abnormal status indicates a poor prognosis. In some embodiments the panel comprises at least 2 genes chosen from the group of genes in at least one of Panels A through G. In some embodiments the panel comprises at least 10 genes chosen from the group of genes in at least one of Panels A through G. In some embodiments the panel comprises at least 15 genes chosen from the group of genes in at least one of Panels A through G. In some embodiments the panel comprises all of the genes in at least one of Panels A through G. The invention also provides a method of determining the prognosis of bladder cancer, comprising determining the status of a panel of genes comprising at least two CCGs (e.g., at least two of the genes in any of Panels B, C, & F), wherein an abnormal status indicates a poor prognosis. The invention also provides a method of determining the prognosis of brain cancer, comprising determining the status of a panel of genes comprising at least two CCGs (e.g., at least two of the genes in any of Panels B, C, & F), wherein an abnormal status indicates a poor prognosis. The invention further provides a method of determining the prognosis of breast cancer, comprising determining the status of a panel of genes comprising at least two CCGs (e.g., at least two of the genes in any of Panels B, C, & F), wherein an abnormal status indicates a poor prognosis. The invention also provides a method of determining the prognosis of lung cancer, comprising determining the status of a panel of genes comprising at least two CCGs (e.g., at least two of the genes in any of Panels B, C, & F), wherein an abnormal status indicates a poor prognosis.

In some embodiments the panel comprises at least 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more CCGs. In some embodiments the panel comprises between 5 and 100 CCGs, between 7 and 40 CCGs, between 5 and 25 CCGs, between 10 and 20 CCGs, or between 10 and 15 CCGs. In some embodiments CCGs comprise at least a certain proportion of the panel. Thus in some embodiments the panel comprises at least 25%, 30%, 40%, 50%, 60%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% CCGs. In some embodiments the CCGs are chosen from the group consisting of the genes listed in Table 1 and Panels A through G. In some embodiments the panel comprises at least 2 genes chosen from the group of genes in any of Panels A through G. In some embodiments the panel comprises at least 10 genes chosen from the group of genes in any of Panels A through G. In some embodiments the panel comprises at least 15 genes chosen from the group of genes in any of Panels A through G. In some embodiments the panel comprises all of the genes in any of Panels A through G.

It has further been discovered that CCG status synergistically adds to clinical parameters in prognosing cancer. In the case of prostate cancer, for example, it has been discovered that a high level of gene expression of any one of the genes in Panels C through F is associated with an increased risk of prostate cancer recurrence or progression in patients whose clinical nomogram score indicates a relatively low risk of recurrence or progression. Because evaluating CCG expression levels can thus detect increased risk not detected using clinical parameters alone, the invention generally provides methods combining evaluating at least one clinical parameter with evaluating the status of at least one CCG.

As Example 3 shows, even individual CCGs add to clinical parameters in predicting cancer recurrence. Thus one aspect of the invention provides an in vitro diagnostic method comprising determining at least one clinical parameter for a cancer patient and determining the status of at least one CCG in a sample obtained from the patient. However, assessing the status of multiple CCGs improves predictive power even more (also shown in Example 1). Thus in some embodiments the status of a plurality of CCGs (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50 or more) is determined. In some embodiments abnormal status indicates an increased likelihood of recurrence or progression. In some embodiments the patient has prostate cancer. In some embodiments the patient has lung cancer. Often the clinical parameter is at least somewhat independently predictive of recurrence or progression and the addition of CCG status improves the predictive power. As used herein, “clinical parameter” and “clinical measure” refer to disease or patient characteristics that are typically applied to assess disease course and/or predict outcome. Examples in cancer generally include tumor stage, tumor grade, lymph node status, histology, performance status, type of surgery, surgical margins, type of treatment, and age of onset. In prostate cancer clinicians often use pre-surgery blood PSA levels, stage (defined by size of tumor and evidence of metastasis), and Gleason score (similar to concept of grade). After surgical intervention, important clinical parameters in prostate cancer include margin and lymph node status. In breast cancer clinicians often use size of index lesion in cm, invasion, number of nodes involved, and grade.

Often certain clinical parameters are correlated with a particular disease character. For example, in cancer generally as well as in specific cancers, certain clinical parameters are correlated with, e.g., likelihood of recurrence or metastasis, prognosis for survival for a certain amount of time, likelihood of response to treatment generally or to a specific treatment, etc. In prostate cancer some clinical parameters are such that their status (presence, absence, level, etc.) is associated with increased likelihood of recurrence. Examples of such recurrence-associated parameters (some but not all of which are specific to prostate cancer) include high PSA levels (e.g., greater than 4 ng/ml), high Gleason score, large tumor size, evidence of metastasis, advanced tumor stage, nuclear grade, lymph node involvement, early age of onset. Other types of cancer may have different parameters correlated to likelihood of recurrence or progression, and CCG status, as a measure of proliferative activity, adds to these parameters in predicting prognosis in these cancers. As used herein, “recurrence-associated clinical parameter” has its conventional meaning for each specific cancer, with which those skilled in the art are quite familiar. In fact, those skilled in the art are familiar with various recurrence-associated clinical parameters beyond those listed here.

Often a physician will assess more than one clinical parameter in a patient and make a more comprehensive evaluation for the disease characters of interest. Example 5 shows how CCG status can add to one particular grouping of clinical parameters used to determine risk of recurrence in prostate cancer. Clinical parameters in Example 5 include binary variables for organ-confined disease and Gleason score less than or equal to 6, and a continuous variable for logarithmic PSA (Table 14). This model includes all of the clinical parameters incorporated in the post-RP nomogram (i.e., Kattan-Stephenson nomogram) except for Year of RP and the two components of the Gleason score. Thus in some embodiments at least two clinical parameters (e.g., two of the above listed parameters) are assessed along with the expression level of at least one CCG.

One way in which single, but more often multiple, clinical parameters are utilized by physicians is with the help of nomograms. In the clinical setting, nomograms are representations (often visual) of a correlation between one or more parameters and one or more patient or disease characters. An example of a prevalent clinical nomogram used in determining a prostate cancer patient's likelihood of recurrence is described in Kattan et al., J. CLIN. ONCOL. (1999) 17:1499-1507, and updated in Stephenson et al., J. CLIN. ONCOL. (2005) 23:7005-7012 (“Kattan-Stephenson nomogram”). This nomogram evaluates a patient by assigning a point value to each of several clinical parameters (year of RP, surgical margins, extracapsular extension, seminal vesicle invasion, lymph node involvement, primary Gleason score, secondary Gleason score, and preoperative PSA level), totalling the points for a patient into a nomogram score, and then predicting the patient's likelihood of being recurrence-free at varying time intervals (up to 10 years) based on this nomogram score. An example of a prevalent clinical nomogram used in determining a breast cancer patient's prognosis for survival is the Nottingham Prognostic Index (NPI). See, e.g., Galea et al., BREAST CANCER RES. & TREAT. (1992) 22:207-19.

It has been discovered that determining the status of a CCG in a sample obtained from a prostate cancer patient, along with the patient's Kattan-Stephenson nomogram score, is a better predictor of 10-year recurrence-free survival than the nomogram score alone. See, e.g., Examples 2 & 5, infra. Specifically, adding CCG status to the Kattan-Stephenson nomogram detects patients at significantly increased risk of recurrence that the nomogram alone does not. Table 3 above provides an exemplary panel of 31 CCGs (Panel C) and a subset panel of 26 CCGs (Panel D, shown with *) determined in Example 2 to show predictive synergy with the Kattan-Stephenson nomogram in prostate cancer prognosis. It has also been discovered that determining the status of a CCG in a sample obtained from a breast cancer patient, along with the patient's NPI score, is a better prognostic predictor than NPI score alone. See, e.g., Example 6, infra. Specifically, adding CCG status to the NPI nomogram detects patients at significantly increased risk of recurrence that the nomogram alone does not. Panels B, C and D were determined in Example 2 to show predictive synergy with the NPI nomogram in breast cancer prognosis.

Thus another aspect of the invention provides an in vitro method comprising determining a clinical nomogram score (e.g., Kattan-Stephenson or NPI nomogram score) for a cancer patient and determining the status of at least one CCG in a sample obtained from the patient. Example 3 illustrates the empirical determination of the predictive power of individual CCGs and of several CCG panels of varying size over the Kattan-Stephenson nomogram. Randomly selected subsets of the 31 CCGs listed in Table 3 were tested as distinct CCG signatures and predictive power (i.e., p-value) was determined for each. As FIG. 1 shows, CCG signatures of 2, 3, 4, 5, 6, 10, 15, 20, 25, and 26 genes each add predictive power to the nomogram. Thus the invention provides a method of determining whether a prostate cancer patient has an increased likelihood of recurrence comprising determining the status of a panel of genes comprising at least 2, 3, 4, 5, 6, 10, 15, 20, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 40, 45, 50, 60, 70, 80, 90, or 100 or more CCGs, wherein an elevated status (e.g., increased expression) for the CCGs indicates an increased likelihood of recurrence. In some embodiments the method further comprises determining a clinical nomogram score of the patient. The invention further provides a method of determining whether a breast cancer patient has an increased likelihood of recurrence comprising determining the status of a panel of genes comprising at least 2, 3, 4, 5, 6, 10, 15, 20, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 40, 45, 50, 60, 70, 80, 90, or 100 or more CCGs, wherein an elevated status (e.g., increased expression) for the CCGs indicates an increased likelihood of recurrence. In some embodiments the method further comprises determining a clinical nomogram score of the patient.

Often clinical nomograms for cancer are designed such that a particular value (e.g., high score) correlates with an increased risk of recurrence. Elevated CCG status (e.g., increased expression or activity) is also correlated with increased risk. Thus, in some embodiments the invention provides a method of determining whether a cancer patient has an increased likelihood of recurrence or progression comprising determining a clinical nomogram score for the patient and determining the status of at least one CCG in a sample obtained from the patient, wherein a high nomogram score and/or an elevated CCG status indicate the patient has an increased likelihood of recurrence or progression. In some embodiments the cancer is prostate cancer. In some embodiments the cancer is lung cancer.

In some embodiments this assessment is made before radical prostatectomy (e.g., using a prostate biopsy sample) while in some embodiments it is made after (e.g., using the resected prostate sample). In some embodiments, a sample of one or more cells are obtained from a prostate cancer patient before or after treatment for analysis according to the present invention. Prostate cancer treatment currently applied in the art includes, e.g., prostatectomy, radiotherapy, hormonal therapy (e.g., using GnRH antagonists, GnRH agonists, antiandrogens), chemotherapy, and high intensity focused ultrasound. In some embodiments, one or more prostate tumor cells from prostate cancer tissue are obtained from a prostate cancer patient during biopsy or prostatectomy and are used for analysis in the method of the present invention.

The present invention is also based on the discovery that PTEN status predicts aggressive prostate cancer. PTEN status adds to both clinical parameters (e.g., Kattan-Stephenson nomogram) and CCGs (e.g., the genes in Table 1 or Panels A through G). As described in more detail in Example 4 below, PTEN status was determined in 191 prostate cancer patient samples with accompanying clinical history data and CCG signature data. Negative PTEN status was found to be a significant predictor for risk of recurrence (p-value 0.031). PTEN remained a significant predictor of recurrence after adjusting for post-surgery clinical parameters and the CCG signature shown in Table 3 (p-value 0.026). In addition, and importantly, the combination of PTEN and the CCG signature seems to be a better predictor of recurrence than post-surgery clinical parameters (p-value 0.0002).

Because PTEN is an independent predictor of prostate cancer recurrence, one aspect of the invention provides a method of predicting a patient's likelihood of prostate cancer recurrence comprising determining PTEN status in a sample from the patient, wherein a low or negative PTEN status indicates the patient has a high likelihood of recurrence. PTEN status can be determined by any technique known in the art, including but not limited to those discussed herein.

Because PTEN adds to CCG status in predicting prostate cancer recurrence, another aspect of the invention provides an in vitro method comprising determining PTEN status and determining the status of a plurality of CCGs in a sample obtained from a patient. Different combinations of techniques can be used to determine the status the various markers. For example, in one embodiment PTEN status is determined by immunohistochemistry (IHC) while the status of the plurality of CCGs is determined by quantitative polymerase chain reaction (qPCR™), e.g., TaqMan™. Some embodiments of the invention provide a method of determining a prostate cancer patient's likelihood of recurrence comprising determining PTEN status in a sample obtained from the patient, determining the status of a plurality of CCGs in a sample obtained from the patient, wherein low or negative PTEN status and/or elevated CCG status indicate the patient has an increased likelihood of recurrence.

Because PTEN status adds predictive value to clinical parameters in predicting prostate recurrence, yet another aspect of the invention provides an in vitro method comprising determining PTEN status and determining at least one clinical parameter for a cancer patient. Often the clinical parameter is at least somewhat independently predictive of recurrence and the addition of PTEN status improves the predictive power. In some embodiments the invention provides a method of determining whether a cancer patient has an increased likelihood of recurrence comprising determining the status of PTEN in a sample obtained from the patient and determining a clinical nomogram score for the patient, wherein low or negative PTEN status and/or a high nomogram score indicate the patient has an increased likelihood of recurrence.

Because all three of the above markers are additive, some embodiments of the invention provide a method of determining whether a cancer patient has an increased likelihood of recurrence comprising determining the status of PTEN in a sample obtained from the patient, determining a clinical nomogram score for the patient and determining the status of at least one CCG in a sample obtained from the patient, wherein low or negative PTEN status, a high nomogram score and an elevated CCG status indicate the patient has an increased likelihood of recurrence.

The results of any analyses according to the invention will often be communicated to physicians, genetic counselors and/or patients (or other interested parties such as researchers) in a transmittable form that can be communicated or transmitted to any of the above parties. Such a form can vary and can be tangible or intangible. The results can be embodied in descriptive statements, diagrams, photographs, charts, images or any other visual forms. For example, graphs showing expression or activity level or sequence variation information for various genes can be used in explaining the results. Diagrams showing such information for additional target gene(s) are also useful in indicating some testing results. The statements and visual forms can be recorded on a tangible medium such as papers, computer readable media such as floppy disks, compact disks, etc., or on an intangible medium, e.g., an electronic medium in the form of email or website on internet or intranet. In addition, results can also be recorded in a sound form and transmitted through any suitable medium, e.g., analog or digital cable lines, fiber optic cables, etc., via telephone, facsimile, wireless mobile phone, internet phone and the like.

Thus, the information and data on a test result can be produced anywhere in the world and transmitted to a different location. As an illustrative example, when an expression level, activity level, or sequencing (or genotyping) assay is conducted outside the United States, the information and data on a test result may be generated, cast in a transmittable form as described above, and then imported into the United States. Accordingly, the present invention also encompasses a method for producing a transmittable form of information on at least one of (a) expression level or (b) activity level for at least one patient sample. The method comprises the steps of (1) determining at least one of (a) or (b) above according to methods of the present invention; and (2) embodying the result of the determining step in a transmittable form. The transmittable form is the product of such a method.

Techniques for analyzing such expression, activity, and/or sequence data (indeed any data obtained according to the invention) will often be implemented using hardware, software or a combination thereof in one or more computer systems or other processing systems capable of effectuating such analysis.

The computer-based analysis function can be implemented in any suitable language and/or browsers. For example, it may be implemented with C language and preferably using object-oriented high-level programming languages such as Visual Basic, SmallTalk, C++, and the like. The application can be written to suit environments such as the Microsoft Windows™ environment including Windows™ 98, Windows™ 2000, Windows™ NT, and the like. In addition, the application can also be written for the MacIntosh™, SUN™, UNIX or LINUX environment. In addition, the functional steps can also be implemented using a universal or platform-independent programming language. Examples of such multi-platform programming languages include, but are not limited to, hypertext markup language (HTML), JAVA™, JavaScript™, Flash programming language, common gateway interface/structured query language (CGI/SQL), practical extraction report language (PERL), AppleScript™ and other system script languages, programming language/structured query language (PL/SQL), and the like. Java™- or JavaScript™-enabled browsers such as HotJava™, Microsoft™ Explorer™, or Netscape™ can be used. When active content web pages are used, they may include Java™ applets or ActiveX™ controls or other active content technologies.

The analysis function can also be embodied in computer program products and used in the systems described above or other computer- or internet-based systems. Accordingly, another aspect of the present invention relates to a computer program product comprising a computer-usable medium having computer-readable program codes or instructions embodied thereon for enabling a processor to carry out gene status analysis. These computer program instructions may be loaded onto a computer or other programmable apparatus to produce a machine, such that the instructions which execute on the computer or other programmable apparatus create means for implementing the functions or steps described above. These computer program instructions may also be stored in a computer-readable memory or medium that can direct a computer or other programmable apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory or medium produce an article of manufacture including instruction means which implement the analysis. The computer program instructions may also be loaded onto a computer or other programmable apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions or steps described above.

Thus one aspect of the present invention provides a system for determining whether a patient has increased likelihood of recurrence. Generally speaking, the system comprises (1) computer means for receiving, storing, and/or retrieving a patient's gene status data (e.g., expression level, activity level, variants) and optionally clinical parameter data (e.g., Gleason score, nomogram score); (2) computer means for querying this patient data; (3) computer means for concluding whether there is an increased likelihood of recurrence based on this patient data; and (4) computer means for outputting/displaying this conclusion. In some embodiments this means for outputting the conclusion may comprise a computer means for informing a health care professional of the conclusion.

One example of such a system is the computer system [600] illustrated in FIG. 6. Computer system [600] may include at least one input module [630] for entering patient data into the computer system [600]. The computer system [600] may include at least one output module for indicating whether a patient has an increased or decreased likelihood of response and/or indicating suggested treatments determined by the computer system [600]. Computer system [600] may include at least one memory module [606] in communication with the at least one input module [630] and the at least one output module [624].

The at least one memory module [606] may include, e.g., a removable storage drive [608], which can be in various forms, including but not limited to, a magnetic tape drive, a floppy disk drive, a VCD drive, a DVD drive, an optical disk drive, etc. The removable storage drive [608] may be compatible with a removable storage unit [610] such that it can read from and/or write to the removable storage unit [610]. Removable storage unit [610] may include a computer usable storage medium having stored therein computer-readable program codes or instructions and/or computer readable data. For example, removable storage unit [610] may store patient data. Example of removable storage unit [610] are well known in the art, including, but not limited to, floppy disks, magnetic tapes, optical disks, and the like. The at least one memory module [606] may also include a hard disk drive [612], which can be used to store computer readable program codes or instructions, and/or computer readable data.

In addition, as shown in FIG. 1, the at least one memory module [606] may further include an interface [614] and a removable storage unit [616] that is compatible with interface [614] such that software, computer readable codes or instructions can be transferred from the removable storage unit [616] into computer system [600]. Examples of interface [614] and removable storage unit [616] pairs include, e.g., removable memory chips (e.g., EPROMs or PROMs) and sockets associated therewith, program cartridges and cartridge interface, and the like. Computer system [600] may also include a secondary memory module [618], such as random access memory (RAM).

Computer system [600] may include at least one processor module [602]. It should be understood that the at least one processor module [602] may consist of any number of devices. The at least one processor module [602] may include a data processing device, such as a microprocessor or microcontroller or a central processing unit. The at least one processor module [602] may include another logic device such as a DMA (Direct Memory Access) processor, an integrated communication processor device, a custom VLSI (Very Large Scale Integration) device or an ASIC (Application Specific Integrated Circuit) device. In addition, the at least one processor module [602] may include any other type of analog or digital circuitry that is designed to perform the processing functions described herein.

As shown in FIG. 6, in computer system [600], the at least one memory module [606], the at least one processor module [602], and secondary memory module [618] are all operably linked together through communication infrastructure [620], which may be a communications bus, system board, cross-bar, etc.). Through the communication infrastructure [620], computer program codes or instructions or computer readable data can be transferred and exchanged. Input interface [626] may operably connect the at least one input module [626] to the communication infrastructure [620]. Likewise, output interface [622] may operably connect the at least one output module [624] to the communication infrastructure [620].

The at least one input module [630] may include, for example, a keyboard, mouse, touch screen, scanner, and other input devices known in the art. The at least one output module [624] may include, for example, a display screen, such as a computer monitor, TV monitor, or the touch screen of the at least one input module [630]; a printer; and audio speakers. Computer system [600] may also include, modems, communication ports, network cards such as Ethernet cards, and newly developed devices for accessing intranets or the internet.

The at least one memory module [606] may be configured for storing patient data entered via the at least one input module [630] and processed via the at least one processor module [602]. Patient data relevant to the present invention may include expression level, activity level, copy number and/or sequence information for PTEN and/or a CCG. Patient data relevant to the present invention may also include clinical parameters relevant to the patient's disease. Any other patient data a physician might find useful in making treatment decisions/recommendations may also be entered into the system, including but not limited to age, gender, and race/ethnicity and lifestyle data such as diet information. Other possible types of patient data include symptoms currently or previously experienced, patient's history of illnesses, medications, and medical procedures.

The at least one memory module [606] may include a computer-implemented method stored therein. The at least one processor module [602] may be used to execute software or computer-readable instruction codes of the computer-implemented method. The computer-implemented method may be configured to, based upon the patient data, indicate whether the patient has an increased likelihood of recurrence, progression or response to any particular treatment, generate a list of possible treatments, etc.

In certain embodiments, the computer-implemented method may be configured to identify a patient as having or not having an increased likelihood of recurrence or progression. For example, the computer-implemented method may be configured to inform a physician that a particular patient has an increased likelihood of recurrence. Alternatively or additionally, the computer-implemented method may be configured to actually suggest a particular course of treatment based on the answers to/results for various queries.

FIG. 7 illustrates one embodiment of a computer-implemented method [700] of the invention that may be implemented with the computer system [600] of the invention. The method [700] begins with one of three queries ([710], [711], [712]), either sequentially or substantially simultaneously. If the answer to/result for any of these queries is “Yes” [720], the method concludes [730] that the patient has an increased likelihood of recurrence. If the answer to/result for all of these queries is “No” [721], the method concludes [731] that the patient does not have an increased likelihood of recurrence. The method [700] may then proceed with more queries, make a particular treatment recommendation ([740], [741]), or simply end.

When the queries are performed sequentially, they may be made in the order suggested by FIG. 7 or in any other order. Whether subsequent queries are made can also be dependent on the results/answers for preceding queries. In some embodiments of the method illustrated in FIG. 7, for example, the method asks about clinical parameters [712] first and, if the patient has one or more clinical parameters identifying the patient as at increased risk for recurrence then the method concludes such [730] or optionally confirms by querying CCG status, while if the patient has no such clinical parameters then the method proceeds to ask about CCG status [711]. Optionally, if CCG status is not elevated, then the method may continue to ask about PTEN status [710]. As mentioned above, the preceding order of queries may be modified. In some embodiments an answer of “yes” to one query (e.g., [712]) prompts one or more of the remaining queries to confirm that the patient has increased risk of recurrence.

In some embodiments, the computer-implemented method of the invention [700] is open-ended. In other words, the apparent first step [710, 711, and/or 712] in FIG. 7 may actually form part of a larger process and, within this larger process, need not be the first step/query. Additional steps may also be added onto the core methods discussed above. These additional steps include, but are not limited to, informing a health care professional (or the patient itself) of the conclusion reached; combining the conclusion reached by the illustrated method [700] with other facts or conclusions to reach some additional or refined conclusion regarding the patient's diagnosis, prognosis, treatment, etc.; making a recommendation for treatment (e.g., “patient should/should not undergo radical prostatectomy”); additional queries about additional biomarkers, clinical parameters, or other useful patient information (e.g., age at diagnosis, general patient health, etc.).

Regarding the above computer-implemented method [700], the answers to the queries may be determined by the method instituting a search of patient data for the answer. For example, to answer the respective queries [710, 711, 712], patient data may be searched for PTEN status (e.g., PTEN IHC or mutation screening), CCG status (e.g., CCG expression level data), or clinical parameters (e.g., Gleason score, nomogram score, etc.). If such a comparison has not already been performed, the method may compare these data to some reference in order to determine if the patient has an abnormal (e.g., elevated, low, negative) status. Additionally or alternatively, the method may present one or more of the queries [710, 711, 712] to a user (e.g., a physician) of the computer system [100]. For example, the questions [710, 711, 712] may be presented via an output module [624]. The user may then answer “Yes” or “No” via an input module [630]. The method may then proceed based upon the answer received. Likewise, the conclusions [730, 731] may be presented to a user of the computer-implemented method via an output module [624].

Thus in some embodiments the invention provides a method comprising: accessing information on a patient's CCG status, clinical parameters and/or PTEN status stored in a computer-readable medium; querying this information to determine at least one of whether a sample obtained from the patient shows increased expression of at least one CCG, whether the patient has a recurrence-associated clinical parameter, and/or whether the patient has a low/negative PTEN status, outputting [or displaying] the sample's CCG expression status, the patient's recurrence-associated clinical parameter status, and/or the sample's PTEN status. As used herein in the context of computer-implemented embodiments of the invention, “displaying” means communicating any information by any sensory means. Examples include, but are not limited to, visual displays, e.g., on a computer screen or on a sheet of paper printed at the command of the computer, and auditory displays, e.g., computer generated or recorded auditory expression of a patient's genotype.

As discussed at length above, recurrence-associated clinical parameters or PTEN status combined with elevated CCG status indicate a significantly increased likelihood of recurrence. Thus some embodiments provide a computer-implemented method of determining whether a patient has an increased likelihood of recurrence comprising accessing information on a patient's PTEN status (e.g., from a tumor sample obtained from the patient) or clinical parameters and CCG status (e.g., from a tumor sample obtained from the patient) stored in a computer-readable medium; querying this information to determine at least one of whether the patient has a low/negative PTEN status or whether the patient has a recurrence-associated clinical parameter; querying this information to determine whether a sample obtained from the patient shows increased expression of at least one CCG; outputting (or displaying) an indication that the patient has an increased likelihood of recurrence if the patient has a low/negative PTEN status or a recurrence-associated clinical parameter and the sample shows increased expression of at least one CCG. Some embodiments further comprise displaying PTEN, clinical parameters (or their values) and/or the CCGs and their status (including, e.g., expression levels), optionally together with an indication of whether the PTEN or CCG status and/or clinical parameter indicates increased likelihood of risk.

The practice of the present invention may also employ conventional biology methods, software and systems. Computer software products of the invention typically include computer readable media having computer-executable instructions for performing the logic steps of the method of the invention. Suitable computer readable medium include floppy disk, CD-ROM/DVD/DVD-ROM, hard-disk drive, flash memory, ROM/RAM, magnetic tapes and etc. Basic computational biology methods are described in, for example, Setubal et al., INTRODUCTION TO COMPUTATIONAL BIOLOGY METHODS (PWS Publishing Company, Boston, 1997); Salzberg et al. (Ed.), COMPUTATIONAL METHODS IN MOLECULAR BIOLOGY, (Elsevier, Amsterdam, 1998); Rashidi & Buehler, BIOINFORMATICS BASICS: APPLICATION IN BIOLOGICAL SCIENCE AND MEDICINE (CRC Press, London, 2000); and Ouelette & Bzevanis, BIOINFORMATICS: A PRACTICAL GUIDE FOR ANALYSIS OF GENE AND PROTEINS (Wiley & Sons, Inc., 2^(nd) ed., 2001); see also, U.S. Pat. No. 6,420,108.

The present invention may also make use of various computer program products and software for a variety of purposes, such as probe design, management of data, analysis, and instrument operation. See U.S. Pat. Nos. 5,593,839; 5,795,716; 5,733,729; 5,974,164; 6,066,454; 6,090,555; 6,185,561; 6,188,783; 6,223,127; 6,229,911 and 6,308,170. Additionally, the present invention may have embodiments that include methods for providing genetic information over networks such as the Internet as shown in U.S. Ser. Nos. 10/197,621 (U.S. Pub. No. 20030097222); 10/063,559 (U.S. Pub. No. 20020183936), 10/065,856 (U.S. Pub. No. 20030100995); 10/065,868 (U.S. Pub. No. 20030120432); 10/423,403 (U.S. Pub. No. 20040049354).

In one aspect, the present invention provides methods of treating a cancer patient comprising obtaining CCG status information (e.g., the CCGs in Table 1 or Panels A through G), and recommending, prescribing or administering a treatment for the cancer patient based on the CCG status. In some embodiments, the method further includes obtaining clinical parameter information, and/or obtaining PTEN status information from a sample from the patient and treating the patient with a particular treatment based on the CCG status, clinical parameter and/or PTEN status information. For example, the invention provides a method of treating a cancer patient comprising:

(1) determining the status of at least one CCG;

(2) determining the status of at least on clinical parameter;

(3) determining the status of PTEN in a sample obtained from the patient; and

(4) recommending, prescribing or administering either

-   -   (a) an active (including aggressive) treatment if the patient         has at least one of increased expression of the CCG,         recurrence-associated clinical parameter, or low/negative PTEN         status, or     -   (b) a passive (or less aggressive) treatment if the patient has         none of increased expression of the CCG, recurrence-associated         clinical parameter, or low/negative PTEN status.

Whether a treatment is aggressive or not will generally depend on the cancer-type, the age of the patient, etc. For example, in breast cancer adjuvant chemotherapy is a common aggressive treatment given to complement the less aggressive standards of surgery and hormonal therapy. Those skilled in the art are familiar with various other aggressive and less aggressive treatments for each type of cancer. “Active treatment” in prostate cancer is well-understood by those skilled in the art and, as used herein, has the conventional meaning in the art. Generally speaking, active treatment in prostate cancer is anything other than “watchful waiting.” Active treatment currently applied in the art of prostate cancer treatment includes, e.g., prostatectomy, radiotherapy, hormonal therapy (e.g., GnRH antagonists, GnRH agonists, antiandrogens), chemotherapy, high intensity focused ultrasound (“HIFU”), etc. Each treatment option carries with it certain risks as well as side-effects of varying severity, e.g., impotence, urinary incontinence, etc. Thus it is common for doctors, depending on the age and general health of the man diagnosed with prostate cancer, to recommend a regime of “watchful-waiting.”

“Watchful-waiting,” also called “active surveillance,” also has its conventional meaning in the art. This generally means observation and regular monitoring without invasive treatment. Watchful-waiting is sometimes used, e.g., when an early stage, slow-growing prostate cancer is found in an older man. Watchful-waiting may also be suggested when the risks of surgery, radiation therapy, or hormonal therapy outweigh the possible benefits. Other treatments can be started if symptoms develop, or if there are signs that the cancer growth is accelerating (e.g., rapidly rising PSA, increase in Gleason score on repeat biopsy, etc.).

Although men who choose watchful-waiting avoid the risks of surgery and radiation, watchful-waiting carries its own risks, e.g., increased risk of metastasis. For younger men, a trial of active surveillance may not mean avoiding treatment altogether, but may reasonably allow a delay of a few years or more, during which time the quality of life impact of active treatment can be avoided. Published data to date suggest that carefully selected men will not miss a window for cure with this approach. Additional health problems that develop with advancing age during the observation period can also make it harder to undergo surgery and radiation therapy. Thus it is clinically important to carefully determine which prostate cancer patients are good candidates for watchful-waiting and which patients should receive active treatment.

Thus, the invention provides a method of treating a prostate cancer patient or providing guidance to the treatment of a patient. In this method, the status of at least one CCG (e.g., those in Table 1 or Panels A through G), at least one recurrence-associated clinical parameter, and/or the status of PTEN is determined, and (a) active treatment is recommended, initiated or continued if a sample from the patient has an elevated status for at least one CCG, the patient has at least one recurrence-associated clinical parameter, and/or low/negative PTEN status, or (b) watchful-waiting is recommended/initiated/continued if the patient has neither an elevated status for at least one CCG, a recurrence-associated clinical parameter, nor low/negative PTEN status. In certain embodiments, CCG status, the clinical parameter(s) and PTEN status may indicate not just that active treatment is recommended, but that a particular active treatment is preferable for the patient (including relatively aggressive treatments such as, e.g., RP and/or adjuvant therapy).

In general, adjuvant therapy (e.g., chemotherapy, radiotherapy, HIFU, hormonal therapy, etc. after prostatectomy or radiotherapy) is not the standard of care in prostate cancer. According to the present invention, however, physicians may be able to determine which prostate cancer patients have particularly aggressive disease and thus should receive adjuvant therapy. Thus in one embodiment, the invention provides a method of treating a patient (e.g., a prostate cancer patient) comprising determining the status of at least one CCG (e.g., those in Table 1 or Panels A through G), the status of at least one recurrence-associated clinical parameter, and/or the status of PTEN and initiating adjuvant therapy after prostatectomy or radiotherapy if a sample from the patient has an elevated status for at least one CCG, the patient has at least one recurrence-associated clinical parameter and/or the patient has low/negative PTEN status.

In one aspect, the invention provides compositions for use in the above methods. Such compositions include, but are not limited to, nucleic acid probes hybridizing to PTEN or a CCG (or to any nucleic acids encoded thereby or complementary thereto); nucleic acid primers and primer pairs suitable for amplifying all or a portion of PTEN or a CCG or any nucleic acids encoded thereby; antibodies binding immunologically to a polypeptide encoded by PTEN or a CCG; probe sets comprising a plurality of said nucleic acid probes, nucleic acid primers, antibodies, and/or polypeptides; microarrays comprising any of these; kits comprising any of these; etc. In some aspects, the invention provides computer methods, systems, software and/or modules for use in the above methods.

In some embodiments the invention provides a probe comprising an isolated oligonucleotide capable of selectively hybridizing to PTEN or at least one of the genes in Table 1 or Panels A through G. The terms “probe” and “oligonucleotide” (also “oligo”), when used in the context of nucleic acids, interchangeably refer to a relatively short nucleic acid fragment or sequence. The invention also provides primers useful in the methods of the invention. “Primers” are probes capable, under the right conditions and with the right companion reagents, of selectively amplifying a target nucleic acid (e.g., a target gene). In the context of nucleic acids, “probe” is used herein to encompass “primer” since primers can generally also serve as probes.

The probe can generally be of any suitable size/length. In some embodiments the probe has a length from about 8 to 200, 15 to 150, 15 to 100, 15 to 75, 15 to 60, or 20 to 55 bases in length. They can be labeled with detectable markers with any suitable detection marker including but not limited to, radioactive isotopes, fluorophores, biotin, enzymes (e.g., alkaline phosphatase), enzyme substrates, ligands and antibodies, etc. See Jablonski et al., NUCLEIC ACIDS RES. (1986) 14:6115-6128; Nguyen et al., BIOTECHNIQUES (1992) 13:116-123; Rigby et al., J. MOL. BIOL. (1977) 113:237-251. Indeed, probes may be modified in any conventional manner for various molecular biological applications. Techniques for producing and using such oligonucleotide probes are conventional in the art.

Probes according to the invention can be used in the hybridization/amplification/detection techniques discussed above. Thus, some embodiments of the invention comprise probe sets suitable for use in a microarray in detecting, amplifying and/or quantitating PTEN and/or a plurality of CCGs. In some embodiments the probe sets have a certain proportion of their probes directed to CCGs—e.g., a probe set consisting of 10%, 20%, 30%, 40%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, or 100% probes specific for CCGs. In some embodiments the probe set comprises probes directed to at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 40, 45, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 250, 300, 350, 400, 450, 500, 600, 700, or 800 or more, or all, of the genes in Table 1 or Panels A through G. Such probe sets can be incorporated into high-density arrays comprising 5,000, 10,000, 20,000, 50,000, 100,000, 200,000, 300,000, 400,000, 500,000, 600,000, 700,000, 800,000, 900,000, or 1,000,000 or more different probes. In other embodiments the probe sets comprise primers (e.g., primer pairs) for amplifying nucleic acids comprising at least a portion of PTEN or of one or more of the CCGs in Table 1 or Panels A through G.

In another aspect of the present invention, a kit is provided for practicing the prognosis of the present invention. The kit may include a carrier for the various components of the kit. The carrier can be a container or support, in the form of, e.g., bag, box, tube, rack, and is optionally compartmentalized. The carrier may define an enclosed confinement for safety purposes during shipment and storage. The kit includes various components useful in determining the status of one or more CCGs and one or more housekeeping gene markers, using the above-discussed detection techniques. For example, the kit many include oligonucleotides specifically hybridizing under high stringency to mRNA or cDNA of the genes in Table 1 or Panels A through G. Such oligonucleotides can be used as PCR primers in RT-PCR reactions, or hybridization probes. In some embodiments the kit comprises reagents (e.g., probes, primers, and or antibodies) for determining the expression level of a panel of genes, where said panel comprises at least 25%, 30%, 40%, 50%, 60%, 75%, 80%, 90%, 95%, 99%, or 100% CCGs (e.g., CCGs in Table 1 or any of Panels A through G). In some embodiments the kit consists of reagents (e.g., probes, primers, and or antibodies) for determining the expression level of no more than 2500 genes, wherein at least 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 150, 200, 250, or more of these genes are CCGs (e.g., CCGs in Table 1 or any of Panels A through G).

The oligonucleotides in the detection kit can be labeled with any suitable detection marker including but not limited to, radioactive isotopes, fluorephores, biotin, enzymes (e.g., alkaline phosphatase), enzyme substrates, ligands and antibodies, etc. See Jablonski et al., Nucleic Acids Res., 14:6115-6128 (1986); Nguyen et al., Biotechniques, 13:116-123 (1992); Rigby et al., J. Mol. Biol., 113:237-251 (1977). Alternatively, the oligonucleotides included in the kit are not labeled, and instead, one or more markers are provided in the kit so that users may label the oligonucleotides at the time of use.

In another embodiment of the invention, the detection kit contains one or more antibodies selectively immunoreactive with one or more proteins encoded by PTEN or one or more CCGs or optionally any additional markers. Examples include antibodies that bind immunologically to PTEN or a protein encoded by a gene in Table 1 or Panels A through G. Methods for producing and using such antibodies have been described above in detail.

Various other components useful in the detection techniques may also be included in the detection kit of this invention. Examples of such components include, but are not limited to, Taq polymerase, deoxyribonucleotides, dideoxyribonucleotides, other primers suitable for the amplification of a target DNA sequence, RNase A, and the like. In addition, the detection kit preferably includes instructions on using the kit for practice the prognosis method of the present invention using human samples.

In a specific embodiment,

Example 1

The following cell cycle gene (CCG) signature was tested for predicting time to chemical recurrence after radical prostatectomy.

31-CCG Prostate Recurrence Signature AURKA BUB1 CCNB1 CCNB2 CDC2 CDC20 CDC45L CDCA8 CENPA CKS2 DLG7 DTL FOXM1 HMMR KIF23 KPNA2 MAD2L1 MELK MYBL2 NUSAP1 PBK PRC1 PTTG1 RRM2 TIMELESS TPX2 TRIP13 TTK UBE2C UBE2S ZWINT

Mean mRNA expression for the above 31 CCGs was tested on 440 prostate tumor FFPE samples using a Cox Proportional Hazard model in Splus 7.1 (Insightful, Inc., Seattle Wash.). The p-value for the likelihood ratio test was 3.98×10⁻⁵.

The mean of CCG expression is robust to measurement error and individual variation between genes. In order to determine the optimal number of cell cycle genes for the signature, the predictive power of the mean was tested for randomly selected sets of from 1 to 30 of the CCGs listed above. This simulation showed that there is a threshold number of CCGs in a panel that provides significantly improved predictive power.

Example 2

In a univariate analysis a set of 31 CCGs (Table 3) was found to be a significant predictor of biochemical recurrence (p-value=1.8×10⁻⁹) after RP in prostate cancer patients. This signature was further evaluated to determine whether it added to an established clinical nomogram for prostate cancer recurrence (the Kattan-Stephenson nomogram). In summary, the nomogram was a highly significant predictor of recurrence (p-value 1.6×10⁻¹⁰) and, after adjusting for the nomogram, the CCG signature was a significant predictor of biochemical recurrence (p-value 4.8×10⁻⁵, Table 6).

Patients and Methods

Eight hundred four consecutive RP patients were followed for a median of 9.5 years. The patient characteristics and the treatment outcomes of the entire cohort have been previously reported (Swanson et al., UROL ONCOL. (2007) 25:110-114). Tissue blocks and/or slides from the final pathological evaluation with enough tissue for analysis were available for 430 patients. The cohort was divided randomly into 212 patients utilized for a training and 199 patient samples as a validation set.

Gene Expression (Statistical methods): Association between biochemical recurrence and CCG expression was evaluated using Cox PH models for time to recurrence. All of the p-values reported in this study were derived from a likelihood ratio test comparing the null model to the model containing the test variable. A set of 31 CCGs (Table 3, supra) was randomly selected. The assays were used to generate expression data from 212 patients in the training set. All of the expression data were generated in triplicate. The expression data were combined into a signature by calculating the mean expression level for 26 CCGs. Association between biochemical recurrence and CCG expression was evaluated using Cox PH models for time to recurrence.

Sample Preparation and Study Design: RNA was isolated from FFPE tumor sections derived from 411 prostate cancer patients treated with RP. Representative 10 μm thick tumor sections were used to isolate RNA. When necessary, a pathologist guided macro- or micro-dissection of the sample was used to enrich for tumor tissue before RNA isolation. None of the samples in the validation cohort were micro-dissected. Prior to any analysis, the cohort was split into 212 patients for initial characterization of the signature (“training set”) and 199 patients for validation. The clinical characteristics of the training and validation cohort are listed on Table 5.

TABLE 5 Training Validation p-value Statistic Age in years at RP, mean (sd) 67.3 (5.9) 66.8 (5.8) 0.355 t-test Ethnicity (% non-white) 2.80% 7.60% 0.042 Fisher's exact Dissection method (% lcm)   24%   0% NA NA Recurrence (%) 71/212 (33.5%) 72/199 (36.2%) 0.605 Fisher's exact Days to recurrence, median 910 822 0.463 t-test Days to follow-up, median 3373 3387 0.173 t-test Pre-surgery PSA (median) 7.3 6.8 0.163 t-test of log Seminal vesicle 23/212 (10.8%) 28/199 (14.1%) 0.37 Fisher's exact Bladder 12/212 (5.7%) 17/199 (8.5%) 0.335 Fisher's exact Lymph node 8/212 (3.8%) 10/199 (5.0%) 0.632 Fisher's exact Capsular 100/212 (47.2%) 101/199 (50.8%) 0.49 Fisher's exact Through capsule 59/212 (27.8%) 66/199 (33.2%) 0.283 Fisher's exact Positive margins 43/212 (20.3%) 57/199 (28.6%) 0.051 Fisher's exact Post-RP Gleason score >6 80/212 (37.7%) 66/199 (33.2%) 0.354 Fisher's exact Post-RP nomogram, mean (sd) 137 (19.5) 138 (23.0) 0.424 t-test

Results

The CCG expression signature (Table 3, supra) was predictive of disease recurrence in a univariate analysis (p-value=1.8×10⁻⁹, Table 6). The distribution of the signature score was skewed toward higher values (lower expression). The median value of signature score was used to divide the training cohort into two groups containing samples with either high or low CCG expression. The survival versus time for both groups is shown in FIG. 2.

Predictive power of the CCG signature after accounting for clinical variables typically included in a post-surgical nomogram (the Kattan-Stephenson nomogram) was also evaluated. The nomogram was a highly significant predictor of recurrence (p-value)1.6×10⁻¹⁰. After adjusting for the nomogram, the CCG signature was a significant predictor of biochemical recurrence (FIG. 3) in the discovery cohort (p-value 0.03) and in the clinical validation cohort (p-value 4.8×10⁻⁵).

TABLE 6 CCG mean p- Recurrence N Co-variates value* Hazard Ratio TRAINING 212 none 0.00404 1.24 (31 CCGs) 204 post-surgery 0.03320 1.16 nomogram VALIDATION 199 none 1.8 × 10⁻⁹ 2.68 (26-CCG subset) 197 post-surgery 4.8 × 10⁻⁵ 1.94 nomogram *Mean of cell cycle gene expression with imputation of missing values, likelihood ratio test for Cox proportional hazards model.

To help understand the interaction between the nomogram and the CCG expression signature, a scatter plot comparing these predictors (FIG. 4) was generated (light gray stars represent patients whose cancer recurred while black stars represent patients whose cancer did not). Analysis of the scatter plot by KM means divided the samples into three clusters based on nomogram score only. Subsequently, it was discovered that the clusters were based on well-understood clinical parameters. The patients in the lowest scoring cluster (116/117) had organ-confined disease. Patients in the middle scoring cluster (48/60) had at least one post-surgical parameter known to be associated with poor outcome (i.e., disease through the capsule, disease positive lymph nodes, and/or disease positive seminal vesicles) and low pre-surgical PSA (<10 ng/ml). Patients in the highest scoring cluster had at least one unfavorable post-surgical parameter and high pre-surgery PSA. Next, the patients in the low and medium scoring clusters were divided by the mean of the CCG score. Outcomes for patients in the highest scoring cluster are adequately predicted by the nomogram and, therefore, were not divided further. As a result, the scatter plot defines five patient groups with disease recurrence rates of 2%, 40% (for two groups), 65%, and 80% (Table 7). The recurrence rate of all five groups versus time is shown in FIG. 5.

TABLE 7 Post-RP nomogram CCG score Low Medium High Low 1/62 (1.6%) 13/31 (41.9%) 16/20 (80%) High 21/55 (38.2%) 19/29 (65.5%)

The scatter plot shown in FIG. 4 suggests that there is a non-linear interaction between the CCG signature and the post-surgical nomogram. That is, the CCG signature is a better predictor in patients with low nomogram scores. Therefore, the study tested for statistical evidence of an interaction between these variables in a multivariate model for predicting disease recurrence (Table 8). There was significant evidence for a favorable interaction in both training and validation studies. Including the interaction term in the model dramatically improved the prognostic significance of the CCG signature after adjusting for the nomogram (p-values of 0.0015 in training and 1.2×10⁻⁸ in validation cohort).

TABLE 8 Statistical Summary Independent Interaction p- Variable Recurrence Cohort N variables Co-variates value p-value Hazard ratio Training 204 nomogram none NA 1.6 × 10⁻¹⁰ 212 CCG none NA 0.004 1.24 signature 204 CCG nomogram 0.021  0.0015 signature Validation 197 nomogram none NA 7.7 × 10⁻¹³ 199 CCG none NA 1.8 × 10⁻⁹ 2.68 signature 197 CCG nomogram 0.0001 1.28 × 10⁻⁸ signature

Example 3

The following study aimed at determining the optimal number of CCGs to include in the signature. As mentioned above, CCG expression levels are correlated to each other so it was possible that measuring a small number of genes would be sufficient to predict disease outcome. In fact, single CCGs from the 31-gene set in Table 3 (Panel C) add significantly to the Kattan-Stephenson nomogram, as shown in Table 9 below (after adjustment for the nomogram and an interaction term between the nomogram and CCG expression):

TABLE 9 Gene p-value* Gene p-value* Gene p-value* NUSAP1 2.8 × 10⁻⁷ BUB1 8.3 × 10⁻⁵ KPNA2 2.0 × 10⁻² DLG7 5.9 × 10⁻⁷ PBK 1.2 × 10⁻⁴ UBE2C 2.2 × 10⁻² CDC2 6.0 × 10⁻⁷ TTK 3.2 × 10⁻⁴ MELK 2.5 × 10⁻² FOXM1 1.1 × 10⁻⁶ CDC45L 7.7 × 10⁻⁴ CENPA 2.9 × 10⁻² MYBL2 1.1 × 10⁻⁶ PRC1 1.2 × 10⁻³ CKS2 5.7 × 10⁻² CDCA8 3.3 × 10⁻⁶ DTL 1.4 × 10⁻³ MAD2L1 1.7 × 10⁻¹ CDC20 3.8 × 10⁻⁶ CCNB1 1.5 × 10⁻³ UBE2S 2.0 × 10⁻¹ RRM2 7.2 × 10⁻⁶ TPX2 1.9 × 10⁻³ AURKA 4.8 × 10⁻¹ PTTG1 1.8 × 10⁻⁵ ZWINT 9.3 × 10⁻³ TIMELESS 4.8 × 10⁻¹ CCNB2 5.2 × 10⁻⁵ KIF23 1.1 × 10⁻² HMMR 5.2 × 10⁻⁵ TRIP13 1.7 × 10⁻² *p-value for likelihood ratio test of full (post-RP nomogram score + cell cycle expression + nomogram:cell cycle) vs reduced (post-RP nomogram score only) CoxPH model of time-to-recurrence.

To evaluate how smaller subsets of the larger CCG set (i.e., smaller CCG panels) performed, the study also compared how well the signature predicted outcome as a function of the number of CCGs included in the signature (FIG. 1). Time to chemical recurrence after prostate surgery was regressed on the CCG mean adjusted by the post-RP nomogram score. Data consist of TLDA assays expressed as deltaCT for 199 FFPE prostate tumor samples and 26 CCGs and were analyzed by a CoxPH multivariate model. P-values are for the likelihood ratio test of the full model (nomogram+cell cycle mean including interaction) vs the reduced model (nomogram only). As shown in Table 10 below and FIG. 1, small CCG signatures (e.g., 2, 3, 4, 5, 6 CCGS, etc.) add significantly to the Kattan-Stephenson nomogram:

TABLE 10 # of CCGs Mean of log10(p-value)* 1 −3.579 2 −4.279 3 −5.049 4 −5.473 5 −5.877 6 −6.228 *For 1000 randomly drawn subsets, size 1 through 6, of cell cycle genes.

Example 4

The aim of this experiment was to evaluate the association between PTEN mutations and biochemical recurrence in prostate cancer patients after radical prostatectomy. Somatic mutations in PTEN were found to be significantly associated with recurrence, and importantly, it added prognostic information beyond both the established clinical nomogram for prostate cancer recurrence (the Kattan-Stephenson nomogram) and the CCG signature score (described in Examples 1 & 2, supra).

Patients and Methods

Eight hundred four consecutive RP patients were followed for a median of 9.5 years. The patient characteristics and the treatment outcomes of the entire cohort have been previously reported (Swanson et al., UROL. ONCOL. (2007) 25:110-114). Tissue blocks and/or slides from the final pathological evaluation with enough tissue for analysis were available for 430 patients. Of these, 191 were selected for PTEN mutation screening based on the amount of available tumor.

Genomic DNA was isolated from the FFPE tumor samples for mutation screening of PTEN using the QIAamp DNA FFPE Tissue kit (Qiagen, Valencia, Calif.) according to the kit protocol. The FFPE slides were first stained with hematoxylin and eosin and examined by a pathologist to identify the tumor region. After deparaffinization, tumor tissue was cut out from the slides by a razor blade. For a few samples dissection was aided by laser capture microscopy (LCM), owing to the dispersion of the tumor cells

Mutations were detected by designing sequencing primers to interrogate the PTEN genomic sequence. The primers contained M13 forward and reverse tails to facilitate sequencing. After amplification, DNA sequence was determined on a Mega BASE 4500 (GE healthcare) using dye-primer chemistry as described in Frank et al., J. CLIN. ONCOL. (2002) 20:1480-1490. Due to the technical difficulties associated with sequencing DNA derived from FFPE material, each mutation was detected by at least two independent amplification and sequencing reactions.

Statistical methods: Unless otherwise specified, the association between biochemical recurrence and PTEN mutations was evaluated using Cox PH models for time to recurrence. The resultant p-values were derived from a likelihood ratio test comparing the null model to the model containing the test variable. In this example (Example 4), the CCG signature was derived from 26 CCGs (Panel D in Table 2, supra). All of the expression data were generated in triplicate. The expression data were combined into a signature by calculating the mean expression level for 26 CCGs. The clinical data were the variables included in the Kattan-Stephenson nomogram.

Results

PTEN mutations were found in 13 individuals (13/191). In this subset of 191 patients, PTEN was a significant predictor of biochemical recurrence (p-value=0.031). The recurrence rate in mutation carriers was 69% (9/13) compared to 36% (64/178) in non-mutant patients. The difference in recurrence rate is also significant using a Fisher's exact test (p-value=0.034). In the subset of patients with clinical parameter data, CCG signature score, and PTEN mutations, PTEN status was a significant predictor of biochemical recurrence after adjusting for both clinical parameters and CCG signature (p-value 0.024). Finally, the combination of PTEN mutation with CCG signature was a better predictor of outcome after adjusting for clinical parameters than using the CCG signature after adjusting for clinical parameters (p-value=0.0002 for the combination compared to 0.0028 for CCG only). These results show that PTEN mutations provide information about the likelihood of recurrence that is uncorrelated with either clinical parameters or CCG signature, and that using all three parameters to evaluate recurrence risk provides a more accurate estimate of recurrence probability than previously possible.

Example 5

This Example describes further studies to validate and refine some embodiments of the CCG signatures of the invention.

Patients and Methods

Eight hundred four consecutive radical prostatectomy patients were followed for a median of 9.5 years. The median age was 67 years. The clinical stage was T1 34%, T2 66% and T3<1%. The median preoperative PSA was 6.6 ng/ml with 72%<10 ng/ml and 28%>10 ng/ml. The specimens were inked and clinical parameters were recorded as to positive bladder neck or urethral margin, invasion into the capsule, extension through the capsule, positive margins and the involvement of the seminal vesicles. Biochemical recurrence was defined as a PSA>0.3 ng/ml. For this study we had access to clinical data on 690 patients. Tissue blocks and/or slides from the final pathological evaluation with enough tissue for analysis were available for 442 patients. The cohort was divided into 195 patients for a training cohort, and 247 patients for validation.

Selection of Genes

Assays of 126 CCGs and 47 HK (housekeeping) genes were run against 96 commercially obtained, anonymous prostate tumor FFPE samples without outcome or other clinical data. The working hypothesis was that the assays would measure with varying degrees of accuracy the same underlying phenomenon (cell cycle proliferation within the tumor for the CCGs, and sample concentration for the HK genes). Assays were ranked by the Pearson's correlation coefficient between the individual gene and the mean of all the candidate genes, that being the best available estimate of biological activity. Results for the correlation of each of the 126 CCGs to the mean are reported in Table 23. Not including CCGs with low average expression, or assays that produced sample failures, approximately half the CCGs had correlations less than 0.58, and a quarter of the HK genes had correlations less than 0.95. These assays were interpreted as not reflecting the underlying phenomenon and were eliminated, leaving a subset of 56 CCGs (Panel G) and 36 HK candidate genes (Tables 11 and 12). Correlation coefficients were recalculated on this subset, and the final selection was made from the ranked list.

TABLE 11 Complete list of evaluated CCGs (“Panel G”) Correlation Gene with CCG Symbol mean FOXM1 0.908 CDC20 0.907 CDKN3 0.9 CDC2 0.899 KIF11 0.898 KIAA0101 0.89 NUSAP1 0.887 CENPF 0.882 ASPM 0.879 BUB1B 0.879 RRM2 0.876 DLGAP5 0.875 BIRC5 0.864 KIF20A 0.86 PLK1 0.86 TOP2A 0.851 TK1 0.837 PBK 0.831 ASF1B 0.827 C18orf24 0.817 RAD54L 0.816 PTTG1 0.814 KIF4A 0.814 CDCA3 0.811 MCM10 0.802 PRC1 0.79 DTL 0.788 CEP55 0.787 RAD51 0.783 CENPM 0.781 CDCA8 0.774 OIP5 0.773 SHCBP1 0.762 ORC6L 0.736 CCNB1 0.727 CHEK1 0.723 TACC3 0.722 MCM4 0.703 FANCI 0.702 KIF15 0.701 PLK4 0.688 APOBEC3B 0.67 NCAPG 0.667 TRIP13 0.653 KIF23 0.652 NCAPH 0.649 TYMS 0.648 GINS1 0.639 STMN1 0.63 ZWINT 0.621 BLM 0.62 TTK 0.62 CDC6 0.619 KIF2C 0.596 RAD51AP1 0.567 NCAPG2 0.535

TABLE 12 List of 15 housekeeping (HK) genes Correaltion Gene with HK Symbol Mean RPL38 0.989 UBA52 0.986 PSMC1 0.985 RPL4 0.984 RPL37 0.983 RPS29 0.983 SLC25A3 0.982 CLTC 0.981 TXNL1 0.98 PSMA1 0.98 RPL8 0.98 MMADHC 0.979 RPL13A; 0.979 LOC728658 PPP2CA 0.978 MRFAP1 0.978

Gene Expression

Total RNA was extracted from representative 5 μM thick FFPE tumor sections. The samples were de-paraffinized using a xylene bath and subsequently hydrated in graded series of ethanol baths. Afterward, the tumor region was dissected from the slide using a razor blade according to the pathologist instructions. Alternatively, the tumor region was dissected directly into an eppendorf tube and the paraffin was removed using xylene and washed with ethanol. After, samples were treated overnight with proteinase K digestion at 55° C. Total RNA was extracted using either RNeasy FFPE or miRNeasy (Qiagen) as described by the manufacturer (with the only exception being the extended proteinase K digestion described above). Isolated total RNA was treated with DNase I (Sigma) prior to cDNA synthesis. Subsequently, we employed the High-capacity cDNA Archive Kit (Applied Biosystems) to convert total RNA into single strand cDNA as described by the manufacturer. A minimum of 200 ng RNA was required for the RT reaction.

Prior to measuring expression levels, the cDNA was pre-amplified with a pooled reaction containing TaqMan™ assays. Pre-amplification reaction conditions were: 14 cycles of 95° C. for 15 sec and 60° C. for 4 minutes. The first cycle was modified to include a 10 minute incubation at 95° C. The amplification reaction was diluted 1:20 using the 1×TE buffer prior to loading on TaqMan™ Low Density Arrays (TLDA, Applied Biosystems) to measure gene expression.

CCG Score

The CCG score is calculated from RNA expression of 31 CCGs (Panel F) normalized by 15 housekeeper genes (HK). The relative numbers of CCGs (31) and HK genes (15) were optimized in order to minimize the variance of the CCG score. The CCG score is the unweighted mean of CT values for CCG expression, normalized by the unweighted mean of the HK genes so that higher values indicate higher expression. One unit is equivalent to a two-fold change in expression. Missing values were imputed using the mean expression for each gene determined in the training set using only good quality samples. The CCG scores were centered by the mean value, again determined in the training set.

A dilution experiment was performed on four of the commercial prostate samples to estimate the measurement error of the CCG score (se=0.10) and the effect of missing values. It was found that the CCG score remained stable as concentration decreased to the point of 5 failures out of the total 31 CCGs. Based on this result, samples with more than 4 missing values were not assigned a CCG score.

The CCG score threshold for determining low-risk was based on the lowest CCG score of recurrences in the training set. The threshold was then adjusted downward by 1 standard deviation in order to optimize the negative predictive value of the test.

Model of Clinical Risk

A Cox proportional hazards model was used to summarize the available clinical parameter data and estimate the prior clinical risk of biochemical recurrence for each patient. The data set consisted of 195 cases from the training set and 248 other cases with clinical parameter information but insufficient sample to measure RNA expression. Univariate tests were performed on clinical parameters known to be associated with outcome (see Table 13 below). Non-significant parameters were excluded from the model. A composite variable was created for organ-confined disease, with invasion defined as surgical margins, extracapsular extension, or involvement of any of seminal vesicles, bladder neck/urethral margins, or lymph nodes. The composite variable for organ-confined disease proved more significant in the model than any of its five components, some of which were inter-correlated or not prevalent. Model fitting was performed using the AIC criteria for post-operative covariates.

TABLE 13 Univariate analysis of clinical parameters and association with biochemical recurrence Cinical Variable p-value* # occurrences Total Frequency BLADDER 0.0002 36 443 0.081 CAPSULAR 1.1 × 10⁻⁹ 194 443 0.438 ETHNICITY 0.6741 416 439 0.948 (WHITE) LYMPHNOD 0.0009 33 443 0.074 MARG.POS 6.1 × 10⁻¹¹ 83 443 0.187 PATHGLEA 6.7 × 10⁻¹⁶ NA 443 NA PATHGRAD 2.4 × 10⁻¹¹ NA 443 NA PATHSTAG 3.1 × 10⁻¹⁵ NA 443 NA PRE.PSA.LOG10 6.2 × 10⁻¹² NA 443 NA SEM.VES 3.0 × 10⁻⁸  56 443 0.126 SURGERY.YEAR 0.0803 NA 443 NA THRU.CAP 1.3 × 10⁻¹⁰ 114 443 0.257 *Cox PH p-value for likelihood ratio test

The final model (i.e., nomogram) has binary variables for organ-confined disease and Gleason score less than or equal to 6, and a continuous variable for logarithmic PSA (Table 14). This model includes all of the clinical parameters incorporated in the post-RP nomogram (i.e., Kattan-Stephenson nomogram) except for Year of RP and the two components of the Gleason score. The distribution of prior clinical risk shows three distinct nodes (FIG. 8). K-means clustering with 3 centers was used to set the threshold for the low-risk cluster, which comprises approximately 50% of the sample.

TABLE 14 Clinical Model Clinical Parameter Coefficient HR p-value* organ-confined disease −0.827 0.44 3.4 × 10⁻⁶ Gleason score ≦6 −0.8734 0.42 4.2 × 10⁻⁷ log PSA 0.6678 1.95 2.0 × 10⁻⁴ *Cox PH p-value for likelihood ratio test

Statistical Analysis

Clinical parameters were compared between the training and validation sets using the Student's t-test for continuous parameters and Fisher's exact test for categorical parameters. The prior clinical risk of patients for biochemical recurrence after surgery was estimated by a post-RP nomogram score summarizing 7 covariates. K-means clustering of the nomogram score was used to categorize patients as low or high prior clinical risk. Expression data were expressed as the CT (the PCR cycle at which the fluorescence intensity exceeds a predetermined threshold) of each CCG normalized by the mean of the 15 housekeeper genes (Table 12 above).

Poor quality samples were excluded from analysis to eliminate poor quality samples or dubious readings without compromising the integrity of the signature by inadvertently excluding samples with low CCG expression. Accordingly, the thresholds for cleaning or filtering the data were set conservatively. Mean expression levels of the HK genes for each sample, which were higher than those of the CCGs, were used to identify poor quality samples. Technical metrics for the amplification efficiency and excessively high standard deviations of replicates were used to identify unreliable CT measurements. No failures of HK genes, and no more than 1 failure out of 3 replicates for CCGs, were allowed.

The association between biochemical recurrence and CCG expression after adjusting for clinical risk predicted by clinical parameters was evaluated using a Cox proportional hazards model for time-to-recurrence. The proportional hazards assumption of no time-dependence was tested for the full model of the CCG signature plus the binary clinical parameter score with an interaction term, and for the CCG signature only in the clinical risk subsets. It was not significant in either training or validation, indicating that there is no evidence for time-dependence. All of the p-values reported are from a likelihood ratio test comparing the reduced or null model to the model containing the test variable. Kaplan-Meier plots are used to show estimated survival probabilities for subsets of patients; however, p-values are from the Cox likelihood ratio test for the continuous values of the variable. All statistical analyses were performed in S+Version 8.1.1 for Linux (TIBCO Spotfire) or R 2.9.0 (http://www.r-project.org).

Results

We isolated RNA from FFPE tumor sections derived from 442 prostate cancer patients treated with RP. The cohort was split into 195 patients for initial characterization of the signature (“training set”) and 247 patients for validation. The clinical parameters of the training and validation cohort are listed in Table 15. There were no significant differences after adjusting for multiple comparisons.

TABLE 15 Clinical parameters of training and validation patient cohorts p- Statistical Clinical Parameter Training Validation value Analysis Age in years at RP, 67.5 (6.2) 66.8 (5.6) 0.204 t-test mean (sd) Ethnicity (% non-white) 3.10% 7.30% 0.058 Fisher's (2 Black, 3 (10 Black, 7 exact Hispanic, 1 Hispanic, 1 other) other) Recurrence 73/195 90/247 0.843 Fisher's (37.4%) (36.4%) exact Days to recurrence, 839 736 0.308 t-test median Days to follow-up, 3300 3332 0.556 t-test median Pre-RP surgery PSA, 7.4 6.4 0.022 t-test of median log Seminal vesicles 23/195 33/247 0.668 Fisher's (11.8%) (13.4%) exact Bladder neck/urethral 12/195 16/247 1 Fisher's margin  (6.2%) (6.5%) exact Lymph nodes 8/195 12/247 0.819 Fisher's  (4.1%)  (4.9%) exact Capsular penetration 104/195 115/247 0.18 Fisher's (53.3%) (46.6%) exact Through the capsule 66/195 73/247 0.354 Fisher's (33.8%) (29.6%) exact Positive margins 51/195 61/247 0.742 Fisher's (26.2%) (24.7%) exact Post-RP Gleason 114/195 166/247 0.06 Fisher's score <7 (58.5%) (67.2%) exact Organ-confined disease 108/195 156/247 0.118 Fisher's (55.4%) (63.2%) exact 10-year PFP (95% CI)   61%   67% 0.905 Log-rank (52%, 69%) (60%, 73%) test

To analyze the CCG signature for this study, we tested 126 CCGs on RNA derived from 96 prostate tumors (Table 11). The tumor samples were anonymous and not associated with clinical data. From this set of genes, we selected 31 genes (Panel F) for inclusion in our signature (Table 16). The genes were selected based on their technical performance, and by how well each gene correlated with the mean expression level of the entire CCG set, in the 96 anonymous samples.

TABLE 16 CCG Signature from Training Set (Panel F) Symbol GeneID Symbol GeneID Symbol GeneID ASF1B 55723 CENPM 79019 ORC6L 23594 ASPM 259266 CEP55 55165 PBK 55872 BIRC5 332 DLGAP5 9787 PLK1 5347 BUB1B 701 DTL 51514 PRC1 9055 C18orf24 220134 FOXM1 2305 PTTG1 9232 CDC2 983 KIAA0101 9768 RAD51 5888 CDC20 991 KIF11 3832 RAD54L 8438 CDCA3 83461 KIF20A 10112 RRM2 6241 CDCA8 55143 MCM10 55388 TK1 7083 CDKN3 1033 NUSAP1 51203 TOP2A 7153 CENPF 1063

To evaluate the prognostic utility of the CCG signature, we generated expression data on 195 patients in the training set. Since the individual gene expression levels were correlated, we combined them into a signature score by calculating the mean expression for the entire set of 31 genes (Panel F), normalized by 15 housekeepers (Table 12). The CCG score distribution was centered at zero, and each score unit corresponds to a 2-fold change in expression level. Poor quality samples were identified by observing either low expression of housekeeping genes or an unacceptable number of CCG failures, and excluded from the analysis. After applying our exclusion rules, there were 140 samples available for analysis. Association between biochemical recurrence and CCG expression was evaluated using Cox PH models for time to recurrence. A high CCG expression value was predictive of disease recurrence in a univariate analysis (p-value=0.01, Table 17).

Next, we evaluated the prognostic utility of the CCG signature after accounting for clinical parameters known to be associated with recurrence after RP. To account for clinical measures in our analysis, we created a model/nomogram that included preoperative PSA, Gleason score, and evidence of disease outside the prostate (i.e., any of either extracapsular extension, or positive post-surgical pathology on lymph nodes, margins, bladder neck, urethral margin or seminal vesicles). The model was optimized in 443 patients (Tables 13 & 14), including all patients for whom we had clinical data but were not in the validation set, and was a highly significant predictor of recurrence in the training cohort (p-value=2.5×10⁻¹¹). The distribution of the scores from the clinical model contained several modes (FIG. 8), separating high- and low-risk patient groups. Therefore, the score was used subsequently as a binary variable (high or low risk). The low-risk cluster correlated with a consistent set of clinical parameters. Specifically, the vast majority (215/218) had organ-confined disease and Gleason score<7. In addition, 80% had low pre-surgical PSA (<10 ng/ml). Patients in the high-risk cluster (N=225) were more heterogeneous, but tended to have clinical characteristics known to be associated with poor outcome (e.g., Gleason>6 and/or disease through the capsule).

Multivariate analysis of the training set incorporating our binary clinical model, showed evidence for a non-linear interaction between the expression signature and clinical parameters (Table 17). To help us understand the nature of this interaction, we generated a scatter plot comparing these predictors (FIG. 8). As evident from the figure, the CCG score proved useful for evaluating recurrence risk in patients defined as low risk by clinical parameters. In fact, even after adjusting for the clinical model within the low risk patients, the CCG signature was a strong predictor of biochemical recurrence (p-value=0.0071).

TABLE 17 Statistical Summary Subset 31-gene training N = 195 31-gene validation N = 247 based on Main Main clin. effect p- Interaction effect p- Interaction model value p-value n value p-value n CCG score 0.01 140 5.8 × 10⁻⁸ 218 Binary clin. 5.1 × 10⁻⁶ 133 1.1 × 10⁻¹⁰ 215 risk (low vs high) CCG score 0.018 0.032 133 8.3 × 10⁻⁷ 0.026 215 adjusted for binary clin. risk + interaction CCG score low-risk 0.0038 54 7.5 × 10⁻⁵ 112 only Clin. risk low-risk 0.22 54 0.044 112 score CCG score low-risk 0.0071 54 0.00019 112 adjusted for clin. risk (clin. risk vs clin. risk + CCG) CCG score high-risk 0.48 79 5.8 × 10⁻⁴ 103 Clin. risk high-risk 2.8 × 10⁻⁶ 79 0.0076 103 score CCG score high-risk 0.51 79 0.0026 103 adjusted for clin. risk (clin. risk vs clin. risk + CCG)

We used our training data in the scatter plot to establish an optimized threshold score of −0.16 for the CCG signature (the mean CCG score is zero). FIG. 12 shows this threshold applied to the 443 patients studied in this example. Forty percent of low-risk patients fall below this threshold, and it was selected so that there were no recurrences 10-years after RP (i.e., negative predictive value (NPV) of 100%). As a result of establishing threshold values for both the clinical model and CCG score, the scatter plot was divided into four sections with recurrence rates of 0% (low CCG) and 26% (high CCG) for low-risk patients; and 60% (low CCG) and 50% for high-risk patients.

Next, we generated CCG expression data on 247 patients in our validation cohort. Thirty-two samples were eliminated from further analysis according to the exclusion rules developed on the training cohort. Panel F was a significant predictor of biochemical recurrence in a univariate analysis (p-value=5.8×10⁻⁸, Table 17). After adjusting for the binary clinical model, the CCG signature was highly predictive of recurrence in the validation cohort (p-value 8.3×10⁻⁷), and as in the training set, there was significant evidence for a non-linear interaction between variables. The CCG signature was informative across the entire spectrum of clinically defined risk (Table 17). In terms of validating the training results, the p-value for association between recurrence and CCG signature in low-risk patients was 1.9×10⁻⁴.

We applied the CCG threshold derived from our analysis of the training cohort to our validation data set (FIG. 9). Low risk patients with CCG scores below the threshold had a 10-year predicted recurrence rate of 5% (equivalent to validated NPV of 0.95). Overall, the combination of CCG score and clinical parameters divided the cohort into four groups with 10 year predicted recurrence rates of 5%, 22%, 36% and 70% (Table 18). The predicted recurrence rate versus CCG score for patients in the validation cohort is shown in FIGS. 10 & 11.

TABLE 18 Summary of recurrence rates in validation cohort defined by clinical risk and CCG score 10-year recurrence rate Clinical CCG Kaplan-Meier risk score estimate n low low 0.05 39 low high 0.22 73 high low 0.36 27 high high 0.7 76

We tested our validated threshold versus various definitions of low-risk patients (Table 19). The signature score was a significant prognostic indicator in a variety of low-risk clinical definitions, and depending on definition, generated a 10-year predicted recurrence rate of 0.05 to 0.10.

TABLE 19 NPV of CCG signature in other definitions of low-risk patients low CCP* 10-yr predicted Clinical definition of low risk recurrence** n p-value*** Organ-confined disease and Gleason 0.05 39 9.4 × 10⁻⁴ score <7 & PSA <10 Organ-confined disease and Gleason 0.08 40 5.8 × 10⁻³ score <7 Organ-confined disease and Gleason 0.07 42 8.7 × 10⁻⁴ score <8 & PSA <10 Organ-confined disease and Gleason 0.1 43 4.1 × 10⁻³ score <8 Organ-confined disease only 0.1 44 2.4 × 10⁻³ *defined by validated threshold **Kaplan-Meier estimates ***for difference between KM estimates for low and high risk adjusted by Greenwood variance.

Comment

We have developed and validated a prognostic molecular signature for prostate cancer. The signature is based on measuring mRNA expression levels of cell cycle genes (CCGs). By definition, expression of CCGs is regulated as a function of cell cycle stage. That is, they are turned on at specific cell cycle stages, so that actively growing cells have higher expression levels of CCG than quiescent cells. Presumably this fact underlies the signature's ability to predict cancer progression. Without wishing to be bound by theory, it is thought that by measuring the expression levels of CCG we are indirectly measuring the growth rate and inherent aggressiveness of the tumor, which ultimately impacts on the likelihood of prostate cancer recurrence after prostatectomy.

There is an important distinction between this study and many others that have attempted to generate prognostic molecular signatures. Often, similar studies begin with a very large number of candidate biomarkers (sometimes exceeding 1000's of genes) that are then evaluated for association with a clinical phenotype of interest. This approach may at times suffer from inherent multiple testing which can make the significance of the derived signature uncertain. Here we have tested a single hypothesis: CCG would be prognostic in prostate cancer (in fact we selected genes based on their correlation with CCG expression, not based on association with recurrence). And since CCG expression is correlated, we combined the expression data into a predictive signature by determining the mean expression value of all the genes in the signature. The simplicity of this approach, biologically and computationally, supports the view that the central claim of this study is likely to be highly robust, and replicated in subsequent studies.

The CCG signature (Panel F) is independently predictive and adds significantly to the predictive power of the clinical parameters typically employed to predict disease recurrence after surgery. This is true in both our training and validation cohorts.

The signature is immediately useful for defining the risk of patients who present with low-risk clinical parameters. Here, we essentially defined low-risk as Gleason<7, PSA<10 and organ-confined disease. The CCG signature score effectively subdivides the low-risk group into patients with very low recurrence rates (5%), and a higher risk of recurrence (22%) (FIG. 9 & Table 18). This is the most dramatic effect of the molecular signature—accurately redefining the risk of patients previously defined as low-risk based on clinical parameters. It is noteworthy that within this patient subpopulation (i.e., patients defined as low-risk based on clinical parameters) clinical parameters are not particularly prognostic (see Table 17). Therefore as a diagnostic test, the signature could be useful for a large number of patients. In this study, nearly 60% of the cohort was characterized as low-risk and 40% of those are expected to have low CCG scores. Therefore, the CCG signature can predict indolent disease in a quarter of the patients who have previously been identified as high-risk (and therefore identified as candidates for radical prostatectomy). Finally, the validation data in particular suggests that the CCG signature may be useful for defining risk in all patients. Specifically, it helped to divide patients defined as high-risk according to clinical parameters into those with 30% and 70% recurrence rates (Table 18).

The combination of clinical parameters and CCG signature enables physicians to more accurately predict risk of surgical failure, and therefore, identify the appropriate course of therapeutic intervention. As we have shown, the signature dramatically improves the recurrence prediction for patients who present with general clinical parameters of non-aggressive disease (Table 19). Within this clinical subgroup, patients with low CCG scores would benefit from the absolute reassurance that no further treatment is indicated. Conversely, the high CCG group may warrant immediate intervention. Patients with unfavorable post-surgical clinical parameters benefit from adjuvant radiation therapy. Therefore the CCG signature should predict the efficacy of adjuvant radiation for patients with low-risk clinical characteristics and high CCG scores. In the validation cohort, patients with high CCG scores and disease beyond the prostate have a recurrence rate of 70%, which should clearly identify patients who are good candidates for adjuvant radiation. Thus the combination of clinical parameters and CCG signature clearly leads to more accurately defined patient risk, which should enable a more intelligent assessment of the need for further treatment.

Example 6

Some of the CCGs panels described herein were further evaluated for their ability to prognose additional cancers. Panels C, D, and F were found to be prognostic to varying degrees in bladder, brain, breast, and lung cancer.

Methods

Gene expression and patient data was obtained from the following publicly available datasets: GSE7390 (Desmedt et al., CLIN. CANCER RES. (2007) 13:3207-14; PMID 17545524); GSE11121 (Schmidt et al., CANCER RES. (2008) 68:5405-13; PMID 18593943); GSE8894 (Son et al.; no publication); Shedden (Shedden et al., NATURE MED. (2008) 14:822; PMID 18641660); GSE4412 (Freije et al., CANCER RES. (2004) 64:6503-10; PMID 15374961); GSE4271 (Phillips et al., CANCER CELL (2006) 9:157-73; PMID 16530701); GSE5287 (Als et al., CLIN. CANCER RES. (2007) 13:4407-14; PMID 17671123). Each of these datasets has an associated detailed description of the experimental procedures used in gathering expression and patient data. The expression microarrays used to generate each dataset are summarized below in Table 20.

TABLE 20 Dataset Array GSE7390 Affymetrix U133 A GSE11121 Affymetrix U133 A GSE8894 Affymetrix U133 plus 2.0 Shedden Affymetrix U133 A GSE4412 Affymetrix U133 A and B GSE4271 Affymetrix U133 A and B GSE5287 Affymetrix U133 A

Expression data for each of the genes in Panels C, D and F was gathered from these datasets and the mean expression level for each Panel was determined for each patient, whose clinical outcome was known (e.g., recurrence, progression, progression-free survival, overall survival, etc.). CCG score is an average expression of the genes in a panel. If a gene is represented by more than one probe set on the array, the gene expression is an average expression of all the probe sets representing the gene. The association between CCG score and survival or disease recurrence was tested using univariate and multivariate Cox proportional hazard model. Multivariate analysis was performed when relevant clinical parameters (grade in brain cancer, stage in lung cancer, NPI in breast cancer) were available.

Results

As shown in Table 21 below, each Panel, in univariate analysis, was a prognostic factor in each of the cancers analyzed.

TABLE 21 p-value Cancer Type Dataset Panel C Panel F Panel B ER positive breast cancer GSE7390 2.4 × 10⁻³ 2.3 × 10⁻³ 4.3 × 10⁻³ ER positive breast cancer GSE11121 1.2 × 10⁻⁵ 8.7 × 10⁻⁶ 1.5 × 10⁻⁵ Lung adenocarcinoma GSE8894 2.0 × 10⁻³ 2.5 × 10⁻³ 5.6 × 10⁻³ Lung adenocarcinoma Shedden 1.3 × 10⁻⁷ 2.6 × 10⁻⁷ 2.2 × 10⁻⁷ Brain cancer GSE4412 3.2 × 10⁻⁵ 2.2 × 10⁻⁵ 9.0 × 10⁻⁵ Brain cancer GSE4271 1.3 × 10⁻³ 1.0 × 10⁻³ 2.8 × 10⁻⁴ Bladder cancer GSE5287 6.4 × 10⁻² 5.0 × 10⁻² 8.6 × 10⁻²

As shown in Table 22 below, each Panel was also prognostic in multivariate analysis when combined with at least one clinical parameter (or nomogram).

TABLE 22 Additional Clinical p-value Variable/ Cancer Type Dataset Panel C Panel F Panel B Nomogram Brain cancer GSE4271 0.022 0.017 0.0065 grade Lung Shedden 1 × 10⁻⁶ 2.1 × 1.4 × 10⁻⁶ stage adeno- 10⁻⁶ carcinoma ER positive GSE7390 0.0077 0.0064 0.011 Nottingham breast Prognostic cancer Index (NPI) ER positive GSE11121 0.0041 0.0027 0.0045 NPI breast cancer

Table 23 below provides a ranking of select CCGs according to their correlation with the mean CCG expression.

TABLE 23 Correl. w/ Gene Symbol Mean TPX2 0.931 CCNB2 0.9287 KIF4A 0.9163 KIF2C 0.9147 BIRC5 0.9077 BIRC5 0.9077 RACGAP1 0.9073 CDC2 0.906 PRC1 0.9053 DLGAP5/DLG7 0.9033 CEP55 0.903 CCNB1 0.9 TOP2A 0.8967 CDC20 0.8953 KIF20A 0.8927 BUB1B 0.8927 CDKN3 0.8887 NUSAP1 0.8873 CCNA2 0.8853 KIF11 0.8723 CDCA8 0.8713 NCAPG 0.8707 ASPM 0.8703 FOXM1 0.87 NEK2 0.869 ZWINT 0.8683 PTTG1 0.8647 RRM2 0.8557 TTK 0.8483 TRIP13 0.841 GINS1 0.841 CENPF 0.8397 HMMR 0.8367 NCAPH 0.8353 NDC80 0.8313 KIF15 0.8307 CENPE 0.8287 TYMS 0.8283 KIAA0101 0.8203 FANCI 0.813 RAD51AP1 0.8107 CKS2 0.81 MCM2 0.8063 PBK 0.805 ESPL1 0.805 MKI67 0.7993 SPAG5 0.7993 MCM10 0.7963 MCM6 0.7957 OIP5 0.7943 CDC45L 0.7937 KIF23 0.7927 EZH2 0.789 SPC25 0.7887 STIL 0.7843 CENPN 0.783 GTSE1 0.7793 RAD51 0.779 CDCA3 0.7783 TACC3 0.778 PLK4 0.7753 ASF1B 0.7733 DTL 0.769 CHEK1 0.7673 NCAPG2 0.7667 PLK1 0.7657 TIMELESS 0.762 E2F8 0.7587 EXO1 0.758 ECT2 0.744 STMN1 0.737 STMN1 0.737 RFC4 0.737 CDC6 0.7363 CENPM 0.7267 MYBL2 0.725 SHCBP1 0.723 ATAD2 0.723 KIFC1 0.7183 DBF4 0.718 CKS1B 0.712 PCNA 0.7103 FBXO5 0.7053 C12orf48 0.7027 TK1 0.7017 BLM 0.701 KIF18A 0.6987 DONSON 0.688 MCM4 0.686 RAD54B 0.679 RNASEH2A 0.6733 TUBA1C 0.6697 C18orf24 0.6697 SMC2 0.6697 CENPI 0.6697 GMPS 0.6683 DDX39 0.6673 POLE2 0.6583 APOBEC3B 0.6513 RFC2 0.648 PSMA7 0.6473 MPHOSPH1/kif20b 0.6457 CDT1 0.645 H2AFX 0.6387 ORC6L 0.634 C1orf135 0.6333 PSRC1 0.633 VRK1 0.6323 CKAP2 0.6307 CCDC99 0.6303 CCNE1 0.6283 LMNB2 0.625 GPSM2 0.625 PAICS 0.6243 MCAM 0.6227 DSN1 0.622 NCAPD2 0.6213 RAD54L 0.6213 PDSS1 0.6203 HN1 0.62 C21orf45 0.6193 CTSL2 0.619 CTPS 0.6183 MCM7 0.618 ZWILCH 0.618 RFC5 0.6177

Table 1 below provides a large, but not exhaustive list of CCGs.

TABLE 1 Gene (Name and/or Symbol) or Number (EST, cDNA clone, or Accession) 1 STK15: serine/threonine kinase 15 Hs.48915 R11407 2 PLK: polo (Drosophia)-like kinase Hs.77597 AA629262 3 UBCH10: ubiquitin carrier protein E2-C Hs.93002 AA430504 4 MAPK13: mitogen-activated protein kinase 13 Hs.178695 AA157499 p38delta mRNA = stress-activated protein kinase 4 5 CDC2: cell division cycle 2, G1 to S and G2 to M Hs.184572 AA598974 6 TOP2A: topoisomerase (DNA) II alpha (170 kD) Hs.156346 AA504348 7 CENPE: centromere protein E (312 kD) Hs.75573 AA402431 CENP-E = putative kinetochore motor that accumulates just befo 8 TOP2A: topoisomerase (DNA) II alpha (170 kD) Hs.156346 AA026682 9 KPNA2: karyopherin alpha 2 (RAG cohort 1, importin alpha 1) Hs.159557 AA676460 10 FLJ10468: hypothetical protein FLJ10468 Hs.48855 N63744 11 CCNF: cyclin F Hs.1973 AA676797 12 DKFZp762E1312: hypothetical protein DKFZp762E1312 Hs.104859 T66935 13 CKS2: CDC2-Associated Protein CKS2 Hs.83758 AA292964 14 C20ORF1: chromosome 20 open reading frame 1 Hs.9329 H73329 15 BUB1: budding uninhibited by benzimidazoles 1 (yeast homolog) Hs.98658 AA430092 BUB1 = putative mitotic checkpoint protein ser/thr kinase 16 TOP2A: **topoisomerase (DNA) II alpha (170 kD) Hs.156346 AI734240 17 CKS2: CDC2-Associated Protein CKS1 Hs.83758 AA010065 ckshs2 = homolog of Cks1 = p34Cdc28/Cdc2-associated protein 18 ARL6IP: ADP-ribosylation factor-like 6 interacting protein Hs.75249 H20558 19 L2DTL: L2DTL protein Hs.126774 R06900 20 STK15: **serine/threonine kinase 15 Hs.48915 H63492 aurora/IPL1-related kinase 21 E2-EPF: ubiquitin carrier protein Hs.174070 AA464019 22 UBCH10: ubiquitin carrier protein E2-C Hs.93002 R80790 23 KNSL5: kinesin-like 5 (mitotic kinesin-like protein 1) Hs.270845 AA452513 Mitotic kinesin-like protein-1 24 CENPF: centromere protein F (350/400 kD, mitosin) Hs.77204 AA701455 25 CCNA2: cyclin A2 Hs.85137 AA608568 Cyclin A 26 CDC2: cell division cycle 2, G1 to S and G2 to M Hs.184572 AA278152 CDC2 = Cell division control protein 2 homolog = P34 protein kin 27 HMMR: **hyaluronan-mediated motility receptor (RHAMM) Hs.72550 AA171715 28 KIAA0008: KIAA0008 gene product Hs.77695 AA262211 29 HSPC145: HSPC145 protein Hs.18349 R22949 30 FLJ20510: hypothetical protein FLJ20510 Hs.6844 N53214 31 Homo sapiens NUF2R mRNA, complete cds Hs.234545 AA421171: 32 HSPC216: hypothetical protein Hs.13525 T87341 33 P37NB: 37 kDa leucine-rich repeat (LRR) protein Hs.155545 AA423870 34 CDC20: 35 CCNE1: cyclin E1 Hs.9700 T54121 36 ESTs: Hs.221754 R84407 37 FLJ11252: hypothetical protein FLJ11252 Hs.23495 N30185 38 LOC51203: clone HQ0310 PRO0310p1 Hs.279905 AA620485 39 FLJ10491: hypothetical protein FLJ10491 Hs.274283 AA425404 40 KNSL1: kinesin-like 1 Hs.8878 AA504625 41 CENPA: centromere protein A (17 kD) Hs.1594 AI369629 42 Homo sapiens, clone IMAGE: 2823731, mRNA, partial cds Hs.70704 R96941: 43 CDC6: CDC6 (cell division cycle 6, S. cerevisiae) homolog Hs.69563 H59203 44 Homo sapiens DNA helicase homolog (PIF1) mRNA, partial cds Hs.112160 AA464521: 45 ESTs: Hs.48480 AA135809 46 TSN: translin Hs.75066 AA460927 47 KPNA2: karyopherin alpha 2 (RAG cohort 1, importin alpha 1) Hs.159557 AA489087 48 RRM2: ribonucleotide reductase M2 polypeptide Hs.75319 AA187351 49 ESTs: Hs.14119 AA204830 50 CCNB1: cyclin B1 Hs.23960 R25788 51 GTSE1: G-2 and S-phase expressed 1 Hs.122552 AI369284 52 C20ORF1: chromosome 20 open reading frame 1 Hs.9329 AA936183 53 TACC3: transforming, acidic coiled-coil containing protein 3 Hs.104019 AA279990 JkR1 mRNA downregulated upon T-cell activation 54 E2F1: E2F transcription factor 1 Hs.96055 H61303 55 BUB1B: budding uninhibited by benzimidazoles 1 (yeast homolog), beta Hs.36708 AA488324 56 ESTs,: Weakly similar to CGHU7L collagen alpha 1(III) chain precursor [H. sapiens] Hs.19322 AA088457 57 KIAA0074: KIAA0074 protein Hs.1192 N54344 58 MPHOSPH1: M-phase phosphoprotein 1 Hs.240 AA282935 59 ANLN: anillin (Drosophila Scraps homolog), actin binding protein Hs.62180 R12261 60 BIRC5: baculoviral IAP repeat-containing 5 (survivin) Hs.1578 AA460685 Survivin = apoptosis inhibitor = effector cell protease EPR-1 61 PTTG1: pituitary tumor-transforming 1 Hs.252587 AA430032 62 KIAA0159: chromosome condensation-related SMC-associated protein 1 Hs.5719 AA668256 63 ESTs,: Weakly similar to OS-4 protein [H. sapiens] Hs.18714 W93120 64 HMMR: hyaluronan-mediated motility receptor (RHAMM) Hs.72550 R10284 65 DKFZp762E1312: hypothetical protein DKFZp762E1312 Hs.104859 AA936181 66 CKAP2: cytoskeleton associated protein 2 Hs.24641 T52152 67 RAMP: RA-regulated nuclear matrix-associated protein 68 SMAP: thyroid hormone receptor coactivating protein Hs.5464 AA481555 69 FLJ22624: hypothetical protein FLJ22624 Hs.166425 AA488791 70 CKS1: CDC2-Associated Protein CKS1 Hs.77550 N48162 71 NEK2: NIMA (never in mitosis gene a)-related kinase 2 Hs.153704 W93379 72 MKI67: antigen identified by monoclonal antibody Ki-67 73 TTK: TTK protein kinase Hs.169840 AI337292 74 VEGFC: vascular endothelial growth factor C Hs.79141 H07899 vascular endothelial growth factor related protein VRP 75 CDKN3: cyclin-dependent kinase inhibitor 3 (CDK2-associated dual specificity phosphatase) Hs.84113 AA284072 CIP2 = Cdi1 = KAP1 phosphatase = G1/S cell cycle gene 76 Homo sapiens NUF2R mRNA, complete cds Hs.234545 R92435: 77 Homo sapiens cDNA FLJ10325 fis, clone NT2RM2000569 Hs.245342 AA235662: 78 HSPC145: HSPC145 protein Hs.18349 AA628867 79 HSU54999: LGN protein Hs.278338 W92010 80 FLJ20333: hypothetical protein FLJ20333 Hs.79828 R27552 81 KNSL2: kinesin-like 2 Hs.20830 N69491 82 ESTs: Hs.133294 AI053446 83 **ESTs: Hs.41294 H95819 84 SMTN: smoothelin Hs.149098 AA449234 85 FLJ23311: hypothetical protein FLJ23311 Hs.94292 N73916 86 USF1: upstream transcription factor 1 Hs.247842 AA719022 87 LOC51203: clone HQ0310 PRO0310p1 Hs.279905 AA779949 88 ADH4: alcohol dehydrogenase 4 (class II), pi polypeptide Hs.1219 AA007395 89 ESTs: Hs.186579 AA960844 90 CCNB2: cyclin B2 Hs.194698 AA774665 91 Homo sapiens, Similar to gene rich cluster, C8 gene, clone MGC: 2577, mRNA, complete cds Hs.30114 AA634371: 92 ESTs: Hs.99480 AA485454 93 Homo sapiens IRE1b mRNA for protein kinase/ribonuclease IRE1 beta, complete cds Hs.114905 AA088442: 94 PCNA: proliferating cell nuclear antigen Hs.78996 AA450264 PCNA = proliferating cell nuclear antigen 95 AA075920: 96 GTSE1: G-2 and S-phase expressed 1 Hs.122552 AA449474 97 CKS1: CDC2-Associated Protein CKS1 Hs.77550 AA278629 98 CDC25B: cell division cycle 25B Hs.153752 AA448659 cdc25B = M-phase inducer phosphatase 2 99 ESTs,: Weakly similar to unnamed protein product [H. sapiens] Hs.99807 AA489023 Unknown UG Hs.99807 ESTs sc_id384 100 PCNA: proliferating cell nuclear antigen Hs.78996 H05891 101 LTBP3: **latent transforming growth factor beta binding protein 3 Hs.289019 R60197 102 Homo sapiens mRNA; cDNA DKFZp434D0818 (from clone DKFZp434D0818) Hs.5855 N95578: 103 ESTs: Hs.126714 AA919126 104 CIT: citron (rho-interacting, serine/threonine kinase 21) Hs.15767 H10788 105 LBR: lamin B receptor Hs.152931 AA099136 106 E2F1: E2F transcription factor 1 Hs.96055 AA424949 107 AA699928: 108 CDKN2C: cyclin-dependent kinase inhibitor 2C (p18, inhibits CDK4) Hs.4854 N72115 p18-INK6 = Cyclin-dependent kinase 6 inhibitor 109 STK12: serine/threonine kinase 12 Hs.180655 H81023 ARK2 = aurora-related kinase 2 110 ESTs: Hs.111471 AA682533 111 ESTs: Hs.44269 AA465090 112 MCM4: minichromosome maintenance deficient (S. cerevisiae) 4 Hs.154443 AA485983 113 PMSCL1: **polymyositis/scleroderma autoantigen 1 (75 kD) Hs.91728 AA458994 Cyclin A 114 MKI67: antigen identified by monoclonal antibody Ki-67 Hs.80976 AA425973 Ki67 (long type) 115 ESTs: Hs.133294 AI144063 116 CDC25B: cell division cycle 25B Hs.153752 H14343 cdc25B = M-phase inducer phosphatase 2 117 FOXM1: forkhead box M1 Hs.239 AA129552 MPP2 = putative M phase phosphoprotein 2 118 FLJ11029: hypothetical protein FLJ11029 Hs.274448 AI124082 119 H2AFX: H2A histone family, member X Hs.147097 H95392 120 FLJ20333: hypothetical protein FLJ20333 Hs.79828 AA147792 121 SLC17A2: solute carrier family 17 (sodium phosphate), member 2 Hs.19710 H60423 122 Homo sapiens IRE1b mRNA for protein kinase/ribonuclease IRE1 beta, complete cds Hs.114905 AA102368: 123 ESTs: Hs.163921 AA573689 124 MCM5: minichromosome maintenance deficient (S. cerevisiae) 5 (cell division cycle 46) Hs.77171 AA283961 125 CDKN1B: cyclin-dependent kinase inhibitor 1B (p27, Kip1) Hs.238990 AA630082 126 AA779865: 127 PTTG1: pituitary tumor-transforming 1 Hs.252587 AI362866 128 RAD21: RAD21 (S. pombe) homolog Hs.81848 AA683102 129 Homo sapiens cDNA FLJ10325 fis, clone NT2RM2000569 Hs.245342 AA430511: 130 NEK2: NIMA (never in mitosis gene a)-related kinase 2 Hs.153704 AA682321 131 FLJ20101: LIS1-interacting protein NUDE1, rat homolog Hs.263925 N79612 132 FZR1: Fzr1 protein Hs.268384 AA621026 133 ESTs: Hs.120605 AI220472 134 KIAA0855: golgin-67 Hs.182982 AA098902 135 SRD5A1: steroid-5-alpha-reductase, alpha polypeptide 1 (3-oxo-5 alpha-steroid delta 4-dehydrogenase alpha 1) Hs.552 H16833 136 RAD51: RAD51 (S. cerevisiae) homolog (E coli RecA homolog) Hs.23044 N70010 137 KNSL2: kinesin-like 2 Hs.20830 R11542 138 KIAA0097: KIAA0097 gene product Hs.76989 AA598942 139 TUBB: tubulin, beta polypeptide Hs.179661 AA427899 140 HEC: highly expressed in cancer, rich in leucine heptad repeats Hs.58169 W72679 141 TROAP: trophinin associated protein (tastin) Hs.171955 H94949 142 ESTs: Hs.49047 N64737 143 ESTs: Hs.15091 AA678348 144 ESTs: Hs.133431 AI061169 145 KIAA0042: KIAA0042 gene product Hs.3104 AA477501 146 FZR1: Fzr1 protein Hs.268384 AA862886 147 FEN1: flap structure-specific endonuclease 1 Hs.4756 AA620553 148 CKS1: CDC2-Associated Protein CKS1 Hs.77550 AA459292 ckshs1 = homolog of Cks1 = p34Cdc28/Cdc2-associated protein 149 ESTs: Hs.193379 N57936 150 CASP8AP2: CASP8 associated protein 2 Hs.122843 H50582 151 BIRC2: baculoviral IAP repeat-containing 2 Hs.289107 R19628 c-IAP1 = MIHB = IAP homolog B 152 CKAP2: cytoskeleton associated protein 2 Hs.24641 AA504130 153 HLA-DRA: major histocompatibility complex, class II, DR alpha Hs.76807 R47979 154 HBP: Hairpin binding protein, histone Hs.75257 AA629558 155 FLJ10483: hypothetical protein FLJ10483 Hs.6877 H12254 156 CASP3: caspase 3, apoptosis-related cysteine protease Hs.74552 R14760 CASPASE- 3 = CPP32 isoform alpha = yama = cysteine protease 157 **ESTs,: Weakly similar to protein that is immuno-reactive with anti-PTH polyclonal antibodies [H. sapiens] Hs.301486 AA088258 158 HMG2: high-mobility group (nonhistone chromosomal) protein 2 Hs.80684 AA019203 159 PRO2000: PRO2000 protein Hs.46677 H58234 160 FLJ20333: hypothetical protein FLJ20333 Hs.79828 T48760 161 T56726: 162 TIMP1: tissue inhibitor of metalloproteinase 1 (erythroid potentiating activity, collagenase inhibitor) Hs.5831 H80214 163 ESTs: Hs.102004 R94281 164 FLJ10858: hypothetical protein FLJ10858 Hs.134403 AA677552 165 Homo sapiens cDNA FLJ11883 fis, clone HEMBA1007178 Hs.157148 N62451: 166 RFC4: replication factor C (activator 1) 4 (37 kD) Hs.35120 N93924 replication factor C 167 PRO2000: PRO2000 protein Hs.46677 N47113 168 ECT2: epithelial cell transforming sequence 2 oncogene Hs.132808 AI031571 169 ESTs: Hs.165909 AA629538 170 PCF11: PCF11p homolog Hs.123654 AA053411 171 BIRC3: baculoviral IAP repeat-containing 3 Hs.127799 H48533 c-IAP2 = MIHC = IAP homolog C = TNFR2-TRAF signalling complex prot 172 EST,: Weakly similar to dJ45P21.2 [H. sapiens] Hs.326451 AA931528 173 KIAA0952: KIAA0952 protein Hs.7935 AA454989 174 KIF5B: kinesin family member 5B Hs.149436 AA608707 175 DKFZP566C134: DKFZP566C134 protein Hs.20237 N39306 176 ANLN: anillin (Drosophila Scraps homolog), actin binding protein Hs.62180 R17092 177 ORC1L: origin recognition complex, subunit 1 (yeast homolog)-like Hs.17908 H51719 178 ESTs: Hs.14139 T77757 179 IFIT1: interferon-induced protein with tetratricopeptide repeats 1 Hs.20315 AA074989 180 MGC5338: hypothetical protein MGC5338 Hs.99598 AA463627 181 COPEB: core promoter element binding protein Hs.285313 AA013481 182 UK114: translational inhibitor protein p14.5 Hs.18426 N72715 183 ESTs: Hs.265592 H67282 184 HMG4: high-mobility group (nonhistone chromosomal) protein 4 Hs.19114 AA670197 185 MDS025: hypothetical protein MDS025 Hs.154938 AI225067 186 DKFZP564A122: DKFZP564A122 protein Hs.187991 N53236 187 TSC22: transforming growth factor beta-stimulated protein TSC-22 Hs.114360 AA664389 188 AAAS: aladin Hs.125262 AA916726 189 PLAG1: **pleiomorphic adenoma gene 1 Hs.14968 AA418251 190 FLJ23293: **hypothetical protein FLJ23293 similar to ARL-6 interacting protein-2 Hs.31236 R91583 191 H11: protein kinase H11; small stress protein-like protein HSP22 Hs.111676 AA010110 192 POLD3: polymerase (DNA directed), delta 3 Hs.82502 AA504204 193 SERPINB3: serine (or cysteine) proteinase inhibitor, clade B (ovalbumin), member 3 Hs.227948 AA292860 194 DNAJB1: DnaJ (Hsp40) homolog, subfamily B, member 1 Hs.82646 AA435948 195 ESTs: Hs.99480 AA458886 196 BUB3: BUB3 (budding uninhibited by benzimidazoles 3, yeast) homolog Hs.40323 AA405690 197 TUBB2: tubulin, beta, 2 Hs.251653 AI000256 198 Homo sapiens SNC73 protein (SNC73) mRNA, complete cds Hs.293441 H28469: 199 BUB3: BUB3 (budding uninhibited by benzimidazoles 3, yeast) homolog Hs.40323 H38804 200 FLJ20699: hypothetical protein FLJ20699 Hs.15125 AA459420 201 KIAA0013: KIAA0013 gene product Hs.172652 N63575 202 ESTs: Hs.20575 N20305 203 CDC25C: cell division cycle 25C Hs.656 W95000 cdc25C = M-phase inducer phosphatase 3 204 FLJ11186: hypothetical protein FLJ11186 Hs.89278 AA394225 205 TOPK: PDZ-binding kinase; T-cell originated protein kinase Hs.104741 AA448898 206 KIAA0165: extra spindle poles, S. cerevisiae, homolog of Hs.153479 AA948058 207 LOC51659: HSPC037 protein Hs.108196 AA961752 208 ESTs: Hs.10338 AA436456 209 SUCLG2: succinate-CoA ligase, GDP-forming, beta subunit Hs.247309 AA465233 210 ZNF265: zinc finger protein 265 Hs.194718 AA452256 211 SKP2: S-phase kinase-associated protein 2 (p45) Hs.23348 R22188 212 NS1-BP: NS1-binding protein Hs.197298 AA486796 213 C21ORF50: chromosome 21 open reading frame 50 Hs.4055 AA416628 214 BIRC2: baculoviral IAP repeat-containing 2 Hs.289107 AA702174 215 BIRC3: baculoviral IAP repeat-containing 3 Hs.127799 AA002125 c- IAP2 = MIHC = IAP homolog C = TNFR2-TRAF signalling complex prot 216 INDO: indoleamine-pyrrole 2,3 dioxygenase Hs.840 AA478279 217 DEEPEST: mitotic spindle coiled-coil related protein Hs.16244 T97349 218 ESTs: Hs.105826 AA534321 219 C20ORF1: chromosome 20 open reading frame 1 Hs.9329 AI654707 220 Homo sapiens cDNA: FLJ21869 fis, clone HEP02442 Hs.28465 R63929: 221 RGS3: regulator of G-protein signalling 3 Hs.82294 AI369623 222 Homo sapiens DC29 mRNA, complete cds Hs.85573 AA186460: 223 MCM6: minichromosome maintenance deficient (mis5, S. pombe) 6 Hs.155462 AA663995 224 NPAT: nuclear protein, ataxia-telangiectasia locus Hs.89385 AA284172 NPAT = E14 = gene in ATM locus 225 KNSL6: kinesin-like 6 (mitotic centromere-associated kinesin) Hs.69360 AA400450 226 HN1: hematological and neurological expressed 1 Hs.109706 AA459865 227 TUBA3: Tubulin, alpha, brain-specific Hs.272897 AA865469 228 ESTs: Hs.221197 N55457 229 KIAA0175: KIAA0175 gene product Hs.184339 AA903137 230 CLASPIN: homolog of Xenopus Claspin Hs.175613 AA857804 231 CTNNA1: **catenin (cadherin-associated protein), alpha 1 (102 kD) Hs.178452 AA026631 232 ESTs: Hs.221962 AA229644 233 SMC4L1: SMC4 (structural maintenance of chromosomes 4, yeast)-like 1 Hs.50758 AA452095 234 ICBP90: transcription factor Hs.108106 AA026356 235 EXO1: exonuclease 1 Hs.47504 AA703000 236 Homo sapiens TRAF4 associated factor 1 mRNA, partial cds Hs.181466 T84975: 237 ESTs: Hs.186814 AA700879 238 FLJ11269: hypothetical protein FLJ11269 Hs.25245 R37817 239 SFPQ: splicing factor proline/glutamine rich (polypyrimidine tract-binding protein- associated) Hs.180610 AA425258 240 ZF: HCF-binding transcription factor Zhangfei Hs.29417 AA164474 241 TUBA2: tubulin, alpha 2 Hs.98102 AA626698 242 Homo sapiens mRNA; cDNA DKFZp434M0435 (from clone DKFZp434M0435) Hs.25700 N94435: 243 FLJ20530: **hypothetical protein FLJ20530 Hs.279521 AA425442 244 BTEB1: basic transcription element binding protein 1 Hs.150557 N80235 245 LOC51053: geminin Hs.234896 H51100 246 D21S2056E: DNA segment on chromosome 21 (unique) 2056 expressed sequence Hs.110757 AI362799 247 HDAC3: histone deacetylase 3 Hs.279789 H88540 248 USP1: ubiquitin specific protease 1 Hs.35086 AA099033 249 C21ORF50: chromosome 21 open reading frame 50 Hs.4055 AA135912 250 FLJ13046: **hypothetical protein FLJ13046 similar to exportin 4 Hs.117102 T95333 251 ESTs: Hs.181059 AA912032 252 FLJ22009: hypothetical protein FLJ22009 Hs.123253 AA401234 253 ESTs: Hs.62711 AA056377 254 RAD51C: RAD51 (S. cerevisiae) homolog C Hs.11393 R37145 RAD51C = Recombination/repair Rad51-related protein 255 ESTs: Hs.268919 H53508 256 Homo sapiens cDNA FLJ11381 fis, clone HEMBA1000501 Hs.127797 AA885096: 257 SAP30: sin3-associated polypeptide, 30 kD Hs.20985 AA126982 258 H4FG: H4 histone family, member G Hs.46423 AA868008 259 TUBA1: tubulin, alpha 1 (testis specific) Hs.75318 AA180742 tubulin-alpha-4 260 DHFR: dihydrofolate reductase Hs.83765 R00884 DHFR = Dihydrofolate reductase 261 DHFR: dihydrofolate reductase Hs.83765 N52980 262 MGC5528: hypothetical protein MGC5528 Hs.315167 AA934904 263 NNMT: nicotinamide N-methyltransferase Hs.76669 T72089 264 TUBB: tubulin, beta polypeptide Hs.179661 AI672565 265 HSPA1L: heat shock 70 kD protein-like 1 Hs.80288 H17513 HSP70-HOM = Heat shock 70 KD protein 1 266 TUBA1: **tubulin, alpha 1 (testis specific) Hs.75318 R36063 267 PRO1073: **PRO1073 protein Hs.6975 AA176999 CIP4 = Cdc42-interacting protein 4 268 POLD3: polymerase (DNA directed), delta 3 Hs.82502 AI017254 269 ESTs,: Moderately similar to T50635 hypothetical protein DKFZp762L0311.1 [H. sapiens] Hs.47378 N38809 270 DKFZP564A122: DKFZP564A122 protein Hs.187991 N57723 271 LRRFIP1: **leucine rich repeat (in FLII) interacting protein 1 Hs.326159 T84633 272 ESTs: Hs.55468 AA165312 273 ESTs: Hs.31444 H16772 274 AFAP: actin filament associated protein Hs.80306 R69355 275 CXCR4: chemokine (C—X—C motif), receptor 4 (fusin) Hs.89414 T62491 CXC chemokine receptor 4 = fusin = neuropeptide Y receptor = L3 276 MSH2: **mutS (E. coli) homolog 2 (colon cancer, nonpolyposis type 1) Hs.78934 AA679697 277 ESTs: Hs.48474 N62074 278 AA677337: 279 ESTs,: Moderately similar to TBB2_HUMAN TUBULIN BETA-2 CHAIN [H. sapiens] Hs.23189 AA629908 280 HP1-BP74: HP1-BP74 Hs.142442 H79795 281 FLJ20101: LIS1-interacting protein NUDE1, rat homolog Hs.263925 AA459394 282 Homo sapiens mRNA; cDNA DKFZp434D1428 (from clone DKFZp434D1428); complete cds Hs.321775 AA431268: 283 ESTs: Hs.265592 AA992658 284 ESTs: 285 DDX11: DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 (S. cerevisiae CHL1- like helicase) Hs.27424 AA402879 286 CDC27: cell division cycle 27 Hs.172405 T81764 287 ARGBP2: Arg/Abl-interacting protein ArgBP2 Hs.278626 N89738 288 DKFZP564A122: DKFZP564A122 protein Hs.187991 AA025807 289 OPN3: opsin 3 (encephalopsin) Hs.279926 AA150060 290 DKFZP566C134: DKFZP566C134 protein Hs.20237 AA456319 291 KIAA0855: golgin-67 Hs.182982 H15101 292 PIN: dynein, cytoplasmic, light polypeptide Hs.5120 AA644679 293 ESTs,: Weakly similar to LIP1_HUMAN PANCREATIC LIPASE RELATED PROTEIN 1 PRECURSO [H. sapiens] Hs.68864 AA088857 294 HDAC3: histone deacetylase 3 Hs.279789 AA973283 295 DONSON: downstream neighbor of SON Hs.17834 AA417895 296 LOC51053: geminin Hs.234896 AA447662 297 FLJ10545: hypothetical protein FLJ10545 Hs.88663 AA460110 298 MAD2L1: MAD2 (mitotic arrest deficient, yeast, homolog)-like 1 Hs.79078 AA481076 mitotic feedback control protein Madp2 homolog 299 TASR2: TLS-associated serine-arginine protein 2 Hs.3530 H11042 300 MCM6: minichromosome maintenance deficient (mis5, S. pombe) 6 Hs.155462 N57722 301 CIT: citron (rho-interacting, serine/threonine kinase 21) Hs.15767 W69425 302 **ESTs: Hs.205066 AA284803 303 ICAM1: intercellular adhesion molecule 1 (CD54), human rhinovirus receptor Hs.168383 R77293 CD54 = ICAM-1 304 KIAA0855: golgin-67 Hs.182982 AA456818 305 ESTs,: Weakly similar to putative p150 [H. sapiens] Hs.300070 R10422 306 DEEPEST: mitotic spindle coiled-coil related protein Hs.16244 AI652290 307 MCM2: minichromosome maintenance deficient (S. cerevisiae) 2 (mitotin) Hs.57101 AA454572 308 Homo sapiens cDNA: FLJ22272 fis, clone HRC03192 Hs.50740 AA495943: 309 WISP1: **WNT1 inducible signaling pathway protein 1 Hs.194680 T54850 310 KIAA0855: golgin-67 Hs.182982 AA280248 311 TEM8: tumor endothelial marker 8 Hs.8966 H58644 312 BITE: p10-binding protein Hs.42315 H96392 313 RAN: RAN, member RAS oncogene family Hs.10842 AA456636 314 EZH2: enhancer of zeste (Drosophila) homolog 2 Hs.77256 AA428252 315 MCM4: minichromosome maintenance deficient (S. cerevisiae) 4 Hs.154443 W74071 316 DKFZp434J0310: hypothetical protein Hs.278408 AA279657 Unknown UG Hs.23595 ESTs sc_id6950 317 PPP1R10: protein phosphatase 1, regulatory subunit 10 Hs.106019 AA071526 318 H11: protein kinase H11; small stress protein-like protein HSP22 Hs.111676 H57493 319 ESTs,: Weakly similar to KIAA1074 protein [H. sapiens] Hs.200483 AA463220 320 ESTs,: Weakly similar to ALU8_HUMAN ALU SUBFAMILY SX SEQUENCE CONTAMINATION WARNING ENTRY [H. sapiens] Hs.226414 N72576 321 AA775033: 322 LOC51004: CGI-10 protein Hs.12239 AA677920 323 ESTs: Hs.150028 AI292036 324 MCM6: minichromosome maintenance deficient (mis5, S. pombe) 6 Hs.155462 AA976533 325 ESTs,: Moderately similar to T50635 hypothetical protein DKFZp762L0311.1 [H. sapiens] Hs.47378 AA406348 326 UCP4: uncoupling protein 4 Hs.40510 H60279 327 MSH5: mutS (E. coli) homolog 5 Hs.112193 AA621155 328 ROCK1: Rho-associated, coiled-coil containing protein kinase 1 Hs.17820 AA872143 329 KIAA0855: golgin-67 Hs.182982 AA694481 330 AA705332: 331 CDC27: cell division cycle 27 Hs.172405 N47994 332 DONSON: downstream neighbor of SON Hs.17834 AI732249 333 SH3GL2: SH3-domain GRB2-like 2 Hs.75149 R12817 334 PRC1: protein regulator of cytokinesis 1 Hs.5101 AA449336 335 ESTs,: Weakly similar to unnamed protein product [H. sapiens] Hs.99807 AA417744 Unknown UG Hs.119424 ESTs sc_id2235 336 Human: clone 23719 mRNA sequence Hs.80305 AA425722 337 Homo sapiens mRNA; cDNA DKFZp564O2364 (from clone DKFZp564O2364) Hs.28893 W90240: 338 ESTs,: Weakly similar to LIP1_HUMAN PANCREATIC LIPASE RELATED PROTEIN 1 PRECURSO [H. sapiens] Hs.68864 AA132858 339 TUBA3: Tubulin, alpha, brain-specific Hs.272897 AA864642 340 AI283530: 341 ESTs: Hs.302878 R92512 342 PPP1R10: protein phosphatase 1, regulatory subunit 10 Hs.106019 T75485 343 SFRS5: splicing factor, arginine/serine-rich 5 Hs.166975 R73672 344 SFRS3: splicing factor, arginine/serine-rich 3 Hs.167460 AA598400 345 PRIM1: primase, polypeptide 1 (49 kD) Hs.82741 AA025937 DNA primase (subunit p48) 346 FLJ20333: hypothetical protein FLJ20333 Hs.79828 H66982 347 HSPA8: heat shock 70 kD protein 8 Hs.180414 AA620511 348 C4A: complement component 4A Hs.170250 AA664406 349 DKC1: dyskeratosis congenita 1, dyskerin Hs.4747 AA052960 350 HP1-BP74: HP1-BP74 Hs.142442 T84669 351 ETV4: ets variant gene 4 (E1A enhancer-binding protein, E1AF) Hs.77711 AA010400 E1A-F = E1A enhancer binding protein = ETS translocation variant 352 Homo sapiens cDNA: FLJ23037 fis, clone LNG02036, highly similar to HSU68019 Homo sapiens mad protein homolog (hMAD-3) mRNA Hs.288261 W42414 Smad3 = hMAD-3 = Homologue of Mothers Against Decapentaplegic (M: 353 KIAA0952: KIAA0952 protein Hs.7935 AA679150 354 STK9: serine/threonine kinase 9 Hs.50905 N80713 355 NXF1: **nuclear RNA export factor 1 Hs.323502 R01238 356 FLJ12892: hypothetical protein FLJ12892 Hs.17731 AA449357 357 UNG: uracil-DNA glycosylase Hs.78853 H15111 358 STK17B: **serine/threonine kinase 17b (apoptosis-inducing) Hs.120996 AA419485 359 YWHAH: tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, eta polypeptide Hs.75544 N69107 360 FLJ13154: hypothetical protein FLJ13154 Hs.25303 AA923560 361 LOC51116: CGI-91 protein Hs.20776 AA459419 362 SSXT: synovial sarcoma, translocated to X chromosome Hs.153221 N59206 363 KIAA0978: KIAA0978 protein Hs.3686 AA485878 364 EST: Hs.147907 AI223432 365 FLJ23468: hypothetical protein FLJ23468 Hs.38178 AA431741 366 FLJ10339: **hypothetical protein FLJ10339 Hs.203963 N95450 367 BMP2: bone morphogenetic protein 2 Hs.73853 AA011061 368 PIR51: RAD51-interacting protein Hs.24596 AI214426 369 FLJ20364: hypothetical protein FLJ20364 Hs.32471 AA676296 370 EIF4A2: **eukaryotic translation initiation factor 4A, isoform 2 Hs.173912 H54751 371 ESTs,: Weakly similar to MCAT_HUMAN MITOCHONDRIAL CARNITINE/ACYLCARNITINE CARRIER PROTEIN [H. sapiens] Hs.27769 AA469975 372 FLJ11323: hypothetical protein FLJ11323 Hs.25625 AA775600 373 DKFZP564D0764: DKFZP564D0764 protein Hs.26799 AA460732 374 CTL2: CTL2 gene Hs.105509 AA454710 375 ESTs: Hs.293419 AA775845 376 IFIT1: interferon-induced protein with tetratricopeptide repeats 1 Hs.20315 AA489640 Interferon-induced 56-KDa protein 377 RBBP8: retinoblastoma-binding protein 8 Hs.29287 H23021 378 **Homo sapiens clone 25061 mRNA sequence Hs.183475 R38944: 379 Human: DNA sequence from clone RP3-383J4 on chromosome 1q24.1-24.3 Contains part of a gene encoding a kelch motif containing protein, part of a novel gene encoding a protein similar to Aspartyl-TRNA sy Hs.117305 N29457 380 FLJ12888: hypothetical protein FLJ12888 Hs.284137 N68390 381 ESTs,: Weakly similar to IF38_HUMAN EUKARYOTIC TRANSLATION INITIATION FACTOR 3 SUBUNIT 8 [H. sapiens] Hs.222088 AI139629 382 ESTs: Hs.241101 AA133590 383 H4FI: H4 histone family, member I Hs.143080 AI218900 384 SP38: zona pellucida binding protein Hs.99875 AA400474 385 GABPB1: GA-binding protein transcription factor, beta subunit 1 (53 kD) Hs.78915 H91651 386 LCHN: LCHN protein Hs.12461 AA029330 387 DKFZP564D0462: hypothetical protein DKFZp564D0462 Hs.44197 N32904 388 LENG8: leukocyte receptor cluster (LRC) encoded novel gene 8 Hs.306121 AA464698 389 HIF1A: hypoxia-inducible factor 1, alpha subunit (basic helix-loop-helix transcription factor) Hs.197540 AA598526 390 ESTs: Hs.93714 R09201 391 FLJ23468: hypothetical protein FLJ23468 Hs.38178 AA454949 392 DKFZP566C134: DKFZP566C134 protein Hs.20237 AA448164 393 PPP3CA: protein phosphatase 3 (formerly 2B), catalytic subunit, alpha isoform (calcineurin A alpha) Hs.272458 W60310 394 HMGE: GrpE-like protein cochaperone Hs.151903 H55907 395 CDK7: cyclin-dependent kinase 7 (homolog of Xenopus MO15 cdk-activating kinase) Hs.184298 R22624 CAK = cdk7 = NRTALRE = sdk = CDK activating kinase 396 ABCC5: **ATP-binding cassette, sub-family C (CFTR/MRP), member 5 Hs.108660 AA186613 397 AA477707: 398 **ESTs: Hs.15607 R92899 399 LOC57209: Kruppel-type zinc finger protein Hs.25275 N50827 400 FLJ20101: LIS1-interacting protein NUDE1, rat homolog Hs.263925 R87716 401 KNSL4: kinesin-like 4 Hs.119324 AA430503 402 E2F5: E2F transcription factor 5, p130-binding Hs.2331 AA455521 E2F-5 = pRB- binding transcription factor 403 TMPO: thymopoietin Hs.11355 T63980 404 POLQ: polymerase (DNA directed), theta Hs.241517 AI057325 405 TGIF: TG-interacting factor (TALE family homeobox) Hs.90077 H51705 406 TRIP13: thyroid hormone receptor interactor 13 Hs.6566 AA630784 407 GAS6: growth arrest-specific 6 Hs.78501 AA461110 408 HN1: hematological and neurological expressed 1 Hs.109706 AA035429 409 BARD1: BRCA1 associated RING domain 1 Hs.54089 AA558464 410 DHFR: dihydrofolate reductase Hs.83765 AA424790 411 AA490946: 412 ESTs: Hs.130435 AA167114 413 HSPA8: heat shock 70 kD protein 8 Hs.180414 AA629567 414 RRM2: ribonucleotide reductase M2 polypeptide Hs.75319 AA826373 415 FLJ20036: hypothetical protein FLJ20036 Hs.32922 H59114 416 COPEB: core promoter element binding protein Hs.285313 AA055584 CPBP = CBA1 = DNA-binding protein 417 FLJ10604: hypothetical protein FLJ10604 Hs.26516 N72697 418 ESTs,: Weakly similar to cDNA EST yk415c12.5 comes from this gene [C. elegans] Hs.108824 H97880 419 UBE2D3: **ubiquitin-conjugating enzyme E2D 3 (homologous to yeast UBC4/5) Hs.118797 AA017199 420 FLJ10890: **hypothetical protein FLJ10890 Hs.17283 AA004210 421 ESTs: Hs.214410 AA579336 422 OLR1: oxidised low density lipoprotein (lectin-like) receptor 1 Hs.77729 AA682386 423 FLJ13231: hypothetical protein FLJ13231 Hs.156148 W92787 424 EST: Hs.323101 W40398 425 ESTs,: Weakly similar to R06F6.5b [C. elegans] Hs.180591 N59330 426 Homo sapiens cDNA: FLJ23285 fis, clone HEP09071 Hs.90424 N26163: 427 Homo sapiens mRNA full length insert cDNA clone EUROIMAGE 42408 Hs.284123 AA211446: 428 NFKB1: nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 (p105) Hs.83428 AA451716 NFkB1 = NF-kappaB p105 = p50 429 LOC58486: transposon-derived Buster1 transposase-like protein Hs.25726 AA630256 430 Homo sapiens cDNA FLJ10976 fis, clone PLACE1001399 Hs.296323 AA424756: 431 KIAA0182: KIAA0182 protein Hs.75909 AI023801 432 RANGAP1: Ran GTPase activating protein 1 Hs.183800 AA991855 433 PKMYT1: membrane-associated tyrosine- and threonine-specific cdc2-inhibitory kinase Hs.77783 AA478066 Myt1 kinase 434 HSPA8: heat shock 70 kD protein 8 Hs.180414 H64096 435 LUC7A: cisplatin resistance-associated overexpressed protein Hs.3688 AA411969 436 RRM1: ribonucleotide reductase M1 polypeptide Hs.2934 AA633549 437 SET07: PR/SET domain containing protein 7 Hs.111988 AA421470 438 **ESTs,: Weakly similar to ALU1_HUMAN ALU SUBFAMILY J SEQUENCE CONTAMINATION WARNING ENTRY [H. sapiens] Hs.193452 W96179 439 Homo sapiens clone 25058 mRNA sequence Hs.179397 R38894: 440 ESTs,: Weakly similar to KIAA0973 protein [H. sapiens] Hs.14014 AA780791 441 EST: Hs.105298 AA489813 442 CTCF: CCCTC-binding factor (zinc finger protein) Hs.57419 H89996 443 HRB: HIV-1 Rev binding protein Hs.171545 AA485958 444 **ESTs: Hs.294083 AA447679 445 KIAA0878: KIAA0878 protein Hs.188006 AA599094 446 ESTs,: Weakly similar to ALUB_HUMAN !!!! ALU CLASS B WARNING ENTRY !!! [H. sapiens] Hs.180552 AA481283 447 OGT: O-linked N-acetylglucosamine (GlcNAc) transferase (UDP-N- acetylglucosamine:polypeptide-N-acetylglucosaminyl transferase) Hs.100293 AA425229 448 Homo sapiens mRNA for KIAA1700 protein, partial cds Hs.20281 N40952: 449 Human: DNA sequence from clone RP1-187J11 on chromosome 6q11.1-22.33. Contains the gene for a novel protein similar to S. pombe and S. cerevisiae predicted proteins, the gene for a novel protein simila Hs.72325 AA159962 450 KIAA1265: KIAA1265 protein Hs.24936 AA479302 451 H1F0: H1 histone family, member 0 Hs.226117 H57830 452 ARGBP2: Arg/Abl-interacting protein ArgBP2 Hs.278626 H02525 453 ODF2: outer dense fibre of sperm tails 2 Hs.129055 AA149882 454 CD97: CD97 antigen Hs.3107 AI651871 455 BMI1: **murine leukemia viral (bmi-1) oncogene homolog Hs.431 AA193573 456 POLG: polymerase (DNA directed), gamma Hs.80961 AA188629 457 XPR1: xenotropic and polytropic retrovirus receptor Hs.227656 AA453474 458 ESTs: Hs.128096 AA971179 459 DNAJB1: DnaJ (Hsp40) homolog, subfamily B, member 1 Hs.82646 AA481022 460 ARL4: ADP-ribosylation factor-like 4 Hs.201672 AI142552 461 SFRS5: splicing factor, arginine/serine-rich 5 Hs.166975 AA598965 462 ESTs: Hs.25933 R11605 463 RIG-I: RNA helicase Hs.145612 AA126958 464 FLJ10339: hypothetical protein FLJ10339 Hs.203963 AA628231 465 DR1: down-regulator of transcription 1, TBP-binding (negative cofactor 2) Hs.16697 AA043503 466 Homo sapiens, Similar to hypothetical protein FLJ20093, clone MGC: 1076, mRNA, complete cds Hs.298998 AA703249: 467 HSPC163: HSPC163 protein Hs.108854 H98963 468 DKFZP564A122: DKFZP564A122 protein Hs.187991 R27345 469 FLJ10128: uveal autoantigen with coiled coil domains and ankyrin repeats Hs.49753 T47624 470 DSCR1: Down syndrome critical region gene 1 Hs.184222 AA629707 471 FLJ10342: hypothetical protein FLJ10342 Hs.101514 AA490935 472 Homo sapiens mRNA; cDNA DKFZp586N1323 (from clone DKFZp586N1323) Hs.24064 R26176: 473 ESTs: Hs.4983 H59921 474 ESTs,: Weakly similar to ALUB_HUMAN !!!! ALU CLASS B WARNING ENTRY !!! [H. sapiens] Hs.117949 H91167 475 CDC45L: CDC45 (cell division cycle 45, S. cerevisiae, homolog)-like Hs.114311 AA700904 476 STAT5B: signal transducer and activator of transcription 5B Hs.244613 AA280647 STAT5A/5B 477 Homo sapiens cDNA FLJ14028 fis, clone HEMBA1003838 Hs.281434 AA454682: 478 KIAA1524: KIAA1524 protein Hs.151343 AI248987 479 CTSD: cathepsin D (lysosomal aspartyl protease) Hs.79572 AA485373 480 Homo sapiens, Similar to hypothetical protein FLJ20093, clone MGC: 1076, mRNA, complete cds Hs.298998 AA682274: 481 GTPBP2: GTP binding protein 2 Hs.13011 T67069 482 LOC51003: CGI-125 protein Hs.27289 AA485945 483 VCL: vinculin Hs.75350 AA486727 484 KIF5B: kinesin family member 5B Hs.149436 AA046613 485 CDC25A: cell division cycle 25A Hs.1634 AA071514 486 LOC51141: insulin induced protein 2 Hs.7089 AA045308 487 **ESTs,: Moderately similar to CALD_HUMAN CALDESMON [H. sapiens] Hs.117774 H48508 488 TBX3-iso: TBX3-iso protein Hs.267182 T48941 489 KIAA0176: KIAA0176 protein Hs.4935 R44371 490 PRKAR1A: protein kinase, cAMP-dependent, regulatory, type I, alpha (tissue specific extinguisher 1) Hs.183037 N25969 PKA-R1 alpha = cAMP-dependent protein kinase type I-alpha-cata 491 ESTs: Hs.268991 H77818 492 ESTs,: Weakly similar to A53028 isopentenyl-diphosphate Delta-isomerase [H. sapiens] Hs.9270 R17362 493 ESTs,: Weakly similar to B34087 hypothetical protein [H. sapiens] Hs.120946 H50656 494 TRN2: karyopherin beta 2b, transportin Hs.278378 R08897 495 LMNA: lamin A/C Hs.77886 AA489582 496 NFE2L2: nuclear factor (erythroid-derived 2)-like 2 Hs.155396 AA629687 497 DKFZp762L0311: hypothetical protein DKFZp762L0311 Hs.16520 AA486418 498 ESTs,: Weakly similar to S71752 giant protein p619 [H. sapiens] Hs.14870 T96829 499 Homo sapiens mRNA; cDNA DKFZp434A1315 (from clone DKFZp434A1315); complete cds Hs.298312 AA991355: 500 E2IG4: hypothetical protein, estradiol-induced Hs.8361 R13844 501 RANGAP1: Ran GTPase activating protein 1 Hs.183800 AA485734 502 H1F0: H1 histone family, member 0 Hs.226117 W69399 503 KIAA0239: KIAA0239 protein Hs.9729 AA454740 504 ESTs,: Weakly similar to ALU7_HUMAN ALU SUBFAMILY SQ SEQUENCE CONTAMINATION WARNING ENTRY [H. sapiens] Hs.68647 R96804 505 PRO0650: PRO0650 protein Hs.177258 N54333 506 DNAJB9: DnaJ (Hsp40) homolog, subfamily B, member 9 Hs.6790 AA045792 507 Homo sapiens cDNA: FLJ21971 fis, clone HEP05790 Hs.71331 AA774678: 508 LOC56996: **cation-chloride cotransporter-interacting protein Hs.119178 AA037466 509 AP3D1: adaptor-related protein complex 3, delta 1 subunit Hs.75056 AA630776 510 SGK: serum/glucocorticoid regulated kinase Hs.159640 AA486082 sgk = putative serine/threonine protein kinase transcriptional 511 HSPC148: hypothetical protein Hs.42743 R23666 512 MRPL19: mitochondrial ribosomal protein L19 Hs.75574 AA521243 KIAA0104 513 AA455102: 514 ESTs: Hs.150325 AI278813 515 **ESTs: Hs.40527 AA029844 516 HSPC145: HSPC145 protein Hs.18349 AI271431 517 KIAA0170: KIAA0170 gene product Hs.277585 H68789 518 FLJ11127: hypothetical protein Hs.91165 T98200 519 KIAA0182: KIAA0182 protein Hs.75909 H05099 520 FLJ23151: hypothetical protein FLJ23151 Hs.137260 AA284259 521 AMD1: S-adenosylmethionine decarboxylase 1 Hs.262476 AA425692 522 FLJ10342: **hypothetical protein FLJ10342 Hs.101514 AA934516 523 SPS: SELENOPHOSPHATE SYNTHETASE; Human selenium donor protein Hs.124027 AA486372 524 KIAA1586: KIAA1586 protein Hs.180663 AA779733 525 ICBP90: transcription factor Hs.108106 AA908902 526 Homo sapiens cDNA: FLJ21971 fis, clone HEP05790 Hs.71331 AI002036: 527 ABCC2: ATP-binding cassette, sub-family C (CFTR/MRP), member 2 Hs.193852 R91502 528 ARHGDIB: Rho GDP dissociation inhibitor (GDI) beta Hs.83656 AA487426 LyGDI = Rho GDP-dissociation inhibitor 2 = RHO GDI 2 529 RAD53: protein kinase Chk2 Hs.146329 AI653182 530 R96880: 531 TNFAIP3: tumor necrosis factor, alpha-induced protein 3 Hs.211600 AA433807 532 ESTs: Hs.26979 H23469 533 AOC2: amine oxidase, copper containing 2 (retina-specific) Hs.143102 N50959 534 Homo sapiens mRNA; cDNA DKFZp586N1323 (from clone DKFZp586N1323) Hs.24064 R30941: 535 AA452872: 536 ESTs: Hs.124169 R58970 537 ACYP1: acylphosphatase 1, erythrocyte (common) type Hs.18573 W78754 538 SIL: TAL1 (SCL) interrupting locus Hs.323032 AA704809 539 AA016234: 540 Homo sapiens mRNA; cDNA DKFZp566P1124 (from clone DKFZp566P1124) Hs.321022 N50895: 541 KIAA1067: KIAA1067 protein Hs.325530 AA099138 542 SMC4L1: SMC4 (structural maintenance of chromosomes 4, yeast)-like 1 Hs.50758 AA283006 543 ESTs: Hs.29074 R70174 544 SNK: serum-inducible kinase Hs.3838 AA460152 545 FANCG: Fanconi anemia, complementation group G Hs.8047 AA427484 546 Homo sapiens cDNA: FLJ21531 fis, clone COL06036 Hs.102941 N95440: 547 Homo sapiens mRNA; cDNA DKFZp547B086 (from clone DKFZp547B086) Hs.36606 N48700: 548 C1ORF2: chromosome 1 open reading frame 2 Hs.19554 H11464 cote1 = ORF in glucocerebrosidase locus 549 HTF9C: HpaII tiny fragments locus 9C Hs.63609 H17888 550 ATF4: activating transcription factor 4 (tax-responsive enhancer element B67) Hs.181243 AA600217 551 ESTs: Hs.101014 AA194941 552 CDC25A: cell division cycle 25A Hs.1634 AA913262 553 TOPK: PDZ-binding kinase; T-cell originated protein kinase Hs.104741 AI002631 554 ASIP: agouti (mouse)-signaling protein Hs.37006 AI220203 555 DKFZP564F013: **hypothetical protein DKFZp564F013 Hs.128653 R14908 556 ZNF265: zinc finger protein 265 Hs.194718 N66014 557 SLC30A1: solute carrier family 30 (zinc transporter), member 1 Hs.55610 AA195463 558 ESTs: Hs.28462 R63922 559 ESTs: Hs.114055 R27431 560 IL6: interleukin 6 (interferon, beta 2) Hs.93913 N98591 IL-6 561 H3F3B: H3 histone, family 3B (H3.3B) Hs.180877 AA608514 562 ESTs: Hs.81263 W81524 563 Homo sapiens cDNA: FLJ23538 fis, clone LNG08010, highly similar to BETA2 Human MEN1 region clone epsilon/beta mRNA Hs.240443 AA400234: 564 AMD1: S-adenosylmethionine decarboxylase 1 Hs.262476 R82299 565 MAP3K2: mitogen-activated protein kinase kinase kinase 2 Hs.28827 AA447971 566 NET1: neuroepithelial cell transforming gene 1 Hs.25155 R24543 567 CHAF1A: chromatin assembly factor 1, subunit A (p150) Hs.79018 AA704459 568 MGC5585: hypothetical protein MGC5585 Hs.5152 H50655 569 KIAA1598: KIAA1598 protein Hs.23740 H17868 570 PNN: pinin, desmosome associated protein Hs.44499 W86139 571 ESTs: Hs.238797 N70848 572 ESTs,: Weakly similar to ALUB_HUMAN !!!! ALU CLASS B WARNING ENTRY !!! [H. sapiens] Hs.180552 AA600192 573 PDGFA: platelet-derived growth factor alpha polypeptide Hs.37040 AA701502 574 Homo sapiens clone FLC0675 PRO2870 mRNA, complete cds Hs.306117 AA443127: 575 ESTs: Hs.143375 AA001841 576 TUBB: tubulin, beta polypeptide Hs.179661 H37989 577 MSH2: mutS (E. coli) homolog 2 (colon cancer, nonpolyposis type 1) Hs.78934 AA219060 MSH2 = DNA mismatch repair mutS homologue 578 TOPBP1: topoisomerase (DNA) II binding protein Hs.91417 R97785 579 KIAA0869: KIAA0869 protein Hs.21543 R43798 580 H4FH: H4 histone family, member H Hs.93758 AA702781 581 FLJ23293: hypothetical protein FLJ23293 similar to ARL-6 interacting protein-2 Hs.31236 AA629027 582 **Homo sapiens cDNA: FLJ23538 fis, clone LNG08010, highly similar to BETA2 Human MEN1 region clone epsilon/beta mRNA Hs.240443 AA053165: 583 KIAA0978: KIAA0978 protein Hs.3686 N64780 584 KIAA1547: KIAA1547 protein Hs.31305 AA057737 585 DKFZP761C169: hypothetical protein DKFZp761C169 Hs.71252 AA608709 586 WS-3: novel RGD-containing protein Hs.39913 AA449975 587 FRZB: frizzled-related protein Hs.153684 H87275 588 BRCA1: breast cancer 1, early onset Hs.194143 H90415 BRCA1 = Mutated in breast and ovarian cancer 589 ESTs: Hs.4983 H22936 590 HSPC150: HSPC150 protein similar to ubiquitin-conjugating enzyme Hs.5199 AA460431 591 Homo sapiens mRNA for KIAA1712 protein, partial cds Hs.29798 H54592: 592 FLJ11186: hypothetical protein FLJ11186 Hs.89278 AA504111 Unknown UG Hs.89278 ESTs 593 ESTs,: Weakly similar to unnamed protein product [H. sapiens] Hs.118338 R25481 594 APEXL2: apurinic/apyrimidinic endonuclease(APEX nuclease)-like 2 protein Hs.154149 AI674393 595 CDR2: cerebellar degeneration-related protein (62 kD) Hs.75124 AA074613 596 ESTs: Hs.69662 AA459724 597 PSCD2L: pleckstrin homology, Sec7 and coiled/coil domains 2-like Hs.8517 AA464957 598 CRK: v-crk avian sarcoma virus CT10 oncogene homolog Hs.306088 H75530 599 CCNE2: cyclin E2 Hs.30464 AA520999 Unknown UG Hs.30464 cyclin E2 600 LOC51240: hypothetical protein Hs.7870 AA988037 601 FLJ11259: hypothetical protein FLJ11259 Hs.184465 AA485877 602 PTP4A1: protein tyrosine phosphatase type IVA, member 1 Hs.227777 AA482193 603 Homo sapiens cDNA: FLJ22355 fis, clone HRC06344 Hs.288283 AA026375: 604 Human: clone 23719 mRNA sequence Hs.80305 H43437 605 Homo sapiens clone FLC0675 PRO2870 mRNA, complete cds Hs.306117 AA485453: 606 MSE55: serum constituent protein Hs.148101 H73234 607 CFLAR: CASP8 and FADD-like apoptosis regulator Hs.195175 AA453766 608 Homo sapiens cDNA: FLJ22844 fis, clone KAIA5181 Hs.296322 AA975103: 609 Human: DNA sequence from clone RP11-371L19 on chromosome 20 Contains two novel genes, the gene for a novel protein similar to 40S ribosomal protein S10 (RPS10), ESTs, STSs, GSSs and five CpG islands Hs.19002 R00846 610 ESTs: Hs.60054 R26390 611 ESTs,: Weakly similar to ALU7_HUMAN ALU SUBFAMILY SQ SEQUENCE CONTAMINATION WARNING ENTRY [H. sapiens] Hs.325158 AA032084 612 FLJ10980: hypothetical protein FLJ10980 Hs.29716 N45467 613 IFIT1: **interferon-induced protein with tetratricopeptide repeats 1 Hs.20315 AA157787 614 ESTs: Hs.21734 AA429809 615 DKFZP434C245: DKFZP434C245 protein Hs.59461 AA705518 616 RNPS1: RNA-binding protein S1, serine-rich domain Hs.75104 AA496837 617 FLJ13639: hypothetical protein FLJ13639 Hs.101821 AA131681 618 PCF11: PCF11p homolog Hs.123654 W73749 619 EIF4G3: eukaryotic translation initiation factor 4 gamma, 3 Hs.25732 N92469 620 Homo sapiens cDNA: FLJ21971 fis, clone HEP05790 Hs.71331 AA130595: 621 STAT1: signal transducer and activator of transcription 1, 91 kD Hs.21486 AA079495 622 BIRC3: baculoviral IAP repeat-containing 3 Hs.127799 R07870 623 HP1-BP74: HP1-BP74 Hs.142442 N20589 624 HSPC228: hypothetical protein Hs.267288 AI734268 625 KIAA0675: KIAA0675 gene product Hs.165662 AA454867 626 AMD1: S-adenosylmethionine decarboxylase 1 Hs.262476 AA504772 627 EST: Hs.149338 AI249089 628 PWP1: nuclear phosphoprotein similar to S. cerevisiae PWP1 Hs.172589 AA485992 629 AI336973: 630 DUSP4: dual specificity phosphatase 4 Hs.2359 AA444049 631 FLJ12788: hypothetical protein FLJ12788 Hs.20242 AA497041 632 HSPC150: HSPC150 protein similar to ubiquitin-conjugating enzyme Hs.5199 AA985450 633 FLJ11729: hypothetical protein FLJ11729 Hs.286212 W15533 634 KLF4: Kruppel-like factor 4 (gut) Hs.7934 H45668 635 FLJ11058: hypothetical protein FLJ11058 Hs.180817 N63911 636 FLJ23468: hypothetical protein FLJ23468 Hs.38178 AA460299 637 ESTs: Hs.115315 AI278336 638 EBI3: Epstein-Barr virus induced gene 3 Hs.185705 AA425028 EBI3 = cytokine receptor 639 ESTs: Hs.293797 N63988 640 MGAT2: mannosyl (alpha-1,6-)-glycoprotein beta-1,2-N- acetylglucosaminyltransferase Hs.172195 AA485653 641 H2BFQ: H2B histone family, member Q Hs.2178 AA456298 642 NMB: neuromedin B Hs.83321 AI650675 643 SSR3: signal sequence receptor, gamma (translocon-associated protein gamma) Hs.28707 AA453486 644 HSPC196: hypothetical protein Hs.239938 R78498 645 EST: Hs.44522 N33610 646 BRF1: butyrate response factor 1 (EGF-response factor 1) Hs.85155 AA723035 647 MAN1A2: mannosidase, alpha, class 1A, member 2 Hs.239114 H97940 648 KIAA1201: KIAA1201 protein Hs.251278 AA427719 649 NUCKS: similar to rat nuclear ubiquitous casein kinase 2 Hs.118064 AA158345 650 MAGEF1: MAGEF1 protein Hs.306123 AA425302 651 Human: Chromosome 16 BAC clone CIT987SK-A-362G6 Hs.6349 N75498 652 R40377: 653 AP3M2: adaptor-related protein complex 3, mu 2 subunit Hs.77770 R14443 654 ESTs,: Weakly similar to 1207289A reverse transcriptase related protein [H. sapiens] Hs.272135 AA705010 655 Homo sapiens mRNA for FLJ00116 protein, partial cds Hs.72363 AA159893: 656 EIF4E: eukaryotic translation initiation factor 4E Hs.79306 AA193254 657 Homo sapiens mRNA for hypothetical protein (TR2/D15 gene) Hs.180545 N47285: 658 ESTs: Hs.99542 AA461474 659 CTNND1: catenin (cadherin-associated protein), delta 1 Hs.166011 AA024656 660 ESTs: Hs.188554 R75884 661 ZNF217: zinc finger protein 217 Hs.155040 R81830 662 FLJ12892: hypothetical protein FLJ12892 Hs.17731 AI243595 663 ETV5: ets variant gene 5 (ets-related molecule) Hs.43697 AA460265 664 EST: Hs.251574 T54821 665 RPS25: ribosomal protein S25 Hs.113029 T98662 666 CNN2: calponin 2 Hs.169718 AA284568 667 ESTs,: Weakly similar to plakophilin 2b [H. sapiens] Hs.12705 AA485365 668 PAPPA: pregnancy-associated plasma protein A Hs.75874 AA609463 669 TFF3: trefoil factor 3 (intestinal) Hs.82961 N74131 670 AI204264: 671 DJ328E19.C1.1: hypothetical protein Hs.218329 AA486041 672 ME3: malic enzyme 3, NADP(+)-dependent, mitochondrial Hs.2838 AA779401 673 ESTs,: Weakly similar to IEFS_HUMAN TRANSFORMATION-SENSITIVE PROTEIN IEF SSP 3521 [H. sapiens] Hs.43213 AA490554 674 FLJ13181: hypothetical protein FLJ13181 Hs.301526 AA057266 675 KIAA1547: KIAA1547 protein Hs.31305 AA136692 676 ZNF281: zinc finger protein 281 Hs.59757 N47468 677 Homo sapiens cDNA: FLJ23260 fis, clone COL05804, highly similar to HSU90911 Human clone 23652 mRNA sequence Hs.13996 AA463961: 678 ESTs: Hs.25933 AA411392 679 NCBP1: nuclear cap binding protein subunit 1, 80 kD Hs.89563 AA278749 nuclear cap binding protein 680 H2BFL: H2B histone family, member L Hs.239884 H70774 681 DKFZP564A122: DKFZP564A122 protein Hs.187991 H66150 682 NASP: nuclear autoantigenic sperm protein (histone-binding) Hs.243886 AA644128 683 **ESTs,: Weakly similar to KIAA0822 protein [H. sapiens] Hs.98368 AA422008 684 MAP2K6: mitogen-activated protein kinase kinase 6 Hs.118825 H07920 685 ESTs: Hs.158357 AA865842 686 GADD45A: growth arrest and DNA-damage-inducible, alpha Hs.80409 AA147214 GADD45 alpha = growth arrest and DNA-damage-inducible protein 687 DHFR: dihydrofolate reductase Hs.83765 AA488803 688 AA151930: 689 Homo sapiens mRNA; cDNA DKFZp434P116 (from clone DKFZp434P116); complete cds Hs.103378 AA431133: 690 Homo sapiens mRNA; cDNA DKFZp564D156 (from clone DKFZp564D156) Hs.9927 T55704: 691 ESTs: Hs.32204 R93719 692 PRPSAP1: phosphoribosyl pyrophosphate synthetase-associated protein 1 Hs.77498 R20005 693 ZNF42: zinc finger protein 42 (myeloid-specific retinoic acid-responsive) Hs.169832 AA987906 694 **ESTs: Hs.43712 N25936 695 RUNX1: runt-related transcription factor 1 (acute myeloid leukemia 1; aml1 oncogene) 695 Hs.129914 AA146826 696 Homo sapiens mRNA; cDNA DKFZp547C244 (from clone DKFZp547C244) Hs.9460 T64452: 697 TYMS: thymidylate synthetase Hs.82962 AA663310 698 MGC5528: hypothetical protein MGC5528 Hs.315167 AA843451 699 ESTs: Hs.268685 R22952 700 SFPQ: splicing factor proline/glutamine rich (polypyrimidine tract-binding protein- associated) Hs.180610 AA418910 701 ESTs: Hs.155105 AI221390 702 FLJ10624: hypothetical protein FLJ10624 Hs.306000 AA489592 703 TRIP8: thyroid hormone receptor interactor 8 Hs.6685 AA425205 704 DNAJB6: DnaJ (Hsp40) homolog, subfamily B, member 6 Hs.181195 AA496105 705 ESTs: Hs.18331 T98244 706 RBM14: RNA binding motif protein 14 Hs.11170 AA421233 707 SCYA2: small inducible cytokine A2 (monocyte chemotactic protein 1, homologous to mouse Sig-je) Hs.303649 AA425102 MCP-1 = MCAF = small inducible cytokine A2 = JE = chemokine 708 MGC4161: hypothetical protein MGC4161 Hs.177688 AI224867 709 TUBB2: tubulin, beta, 2 Hs.251653 AA888148 710 FLJ20280: hypothetical protein FLJ20280 Hs.270134 N74086 711 TERA: TERA protein Hs.180780 AA465096 712 CPS1: **carbamoyl-phosphate synthetase 1, mitochondrial Hs.50966 N68399 713 KIAA0802: KIAA0802 protein Hs.27657 W55875 714 FYN: FYN oncogene related to SRC, FGR, YES Hs.169370 N22980 715 Homo sapiens PRO2751 mRNA, complete cds Hs.283978 H12784: 716 CLTH: Clathrin assembly lymphoid-myeloid leukemia gene Hs.7885 AA441930 717 CHMP1.5: CHMP1.5 protein Hs.42733 W85875 718 SMARCB1: SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily b, member 1 Hs.159971 AA446018 719 AA487823: SRF = c-fos serum response element-binding transcription facto 720 **ESTs: Hs.130741 AA608725 721 Homo sapiens cDNA FLJ10976 fis, clone PLACE1001399 Hs.296323 R36085: 722 FLJ20036: hypothetical protein FLJ20036 Hs.32922 N91145 723 C11ORF5: chromosome 11 open reading frame 5 Hs.121025 AA776702 724 AF3P21: SH3 protein Hs.102929 N94372 725 LOC54104: hypothetical protein Hs.12871 H05934 726 DF: D component of complement (adipsin) Hs.155597 AA233549 727 CEP4: Cdc42 effector protein 4; binder of Rho GTPases 4 Hs.3903 AA449061 728 KIF5B: kinesin family member 5B Hs.149436 AA644218 729 MGC5627: hypothetical protein MGC5627 Hs.237971 H02336 730 G3BP: Ras-GTPase-activating protein SH3-domain-binding protein Hs.220689 AA449834 731 ESTs: Hs.293987 AA229758 732 ESTs: Hs.36828 AA194796 733 Homo sapiens mRNA for FLJ00101 protein, partial cds Hs.221600 W92262: 734 Homo sapiens cDNA: FLJ21288 fis, clone COL01927 Hs.6019 R07184: 735 ESTs,: Weakly similar to 1207289A reverse transcriptase related protein [H. sapiens] Hs.250594 H86813 736 Homo sapiens cDNA FLJ11941 fis, clone HEMBB1000649 Hs.124106 AI301573: 737 ESTs: Hs.24908 H77726 738 TOB2: transducer of ERBB2, 2 Hs.4994 AA486088 739 ESTs: Hs.143900 AI193212 740 Homo sapiens clone FLC0675 PRO2870 mRNA, complete cds Hs.306117 H16589: 741 ESTs,: Weakly similar to KIAA0638 protein [H. sapiens] Hs.296288 T83657 742 FLJ20039: hypothetical protein FLJ20039 Hs.267448 AA448268 743 RPA2: replication protein A2 (32 kD) Hs.79411 R13557 744 GAS1: growth arrest-specific 1 Hs.65029 AA025819 745 Human: DNA sequence from clone 967N21 on chromosome 20p12.3-13. Contains the CHGB gene for chromogranin B (secretogranin 1, SCG1), a pseudogene similar to part of KIAA0172, the gene for a novel protein Hs.88959 R56678 746 ESTs: Hs.21175 AI341642 747 LBC: lymphoid blast crisis oncogene Hs.301946 AA135716 748 ESTs: Hs.194595 R06761 749 MGC4707: hypothetical protein MGC4707 Hs.291003 R14653 750 ZNF183: zinc finger protein 183 (RING finger, C3HC4 type) Hs.64794 AA132766 751 RAD18: postreplication repair protein hRAD18p Hs.21320 R59197 752 EIF4EBP2: **eukaryotic translation initiation factor 4E binding protein 2 Hs.278712 H15159 753 **Homo sapiens mRNA; cDNA DKFZp586M0723 (from clone DKFZp586M0723) Hs.27860 AA446650: 754 ORC3L: origin recognition complex, subunit 3 (yeast homolog)-like Hs.74420 H99257 755 CDK7: cyclin-dependent kinase 7 (homolog of Xenopus MO15 cdk-activating kinase) Hs.184298 AI311067 756 USP10: ubiquitin specific protease 10 Hs.78829 AA455233 757 KIAA0733: TAK1-binding protein 2; KIAA0733 protein Hs.109727 AA931658 758 R89286: 759 ALDH4: aldehyde dehydrogenase 4 (glutamate gamma-semialdehyde dehydrogenase; pyrroline-5-carboxylate dehydrogenase) Hs.77448 AA181378 760 IDN3: IDN3 protein Hs.225767 N62911 761 ESTs: Hs.50180 H48143 762 MIG2: mitogen inducible 2 Hs.75260 H29252 763 KIAA0856: KIAA0856 protein Hs.13264 R12847 764 EST: Hs.47763 N54162 765 Homo sapiens mRNA; cDNA DKFZp547C244 (from clone DKFZp547C244) Hs.9460 AA447553: 766 KIAA0855: golgin-67 Hs.182982 AA775625 767 ESTs,: Weakly similar to JH0148 nucleolin - rat [R. norvegicus] Hs.30120 R54659 768 FLJ22313: hypothetical protein FLJ22313 Hs.30211 H52061 769 ESTs: Hs.71818 AI028074 770 KIAA0618: KIAA0618 gene product Hs.295112 AA455506 771 ESTs: Hs.59413 W93056 772 ESTs: Hs.165607 AA992090 773 UBAP: ubiquitin associated protein Hs.75425 AA446016 774 HAN11: WD-repeat protein Hs.176600 AA725641 775 USP16: ubiquitin specific protease 16 Hs.99819 AA489619 776 ESTs: Hs.67776 AA464963 777 SM-20: similar to rat smooth muscle protein SM-20 Hs.6523 H56028 778 CCNG2: cyclin G2 Hs.79069 AA489647 779 Homo sapiens mRNA; cDNA DKFZp566P1124 (from clone DKFZp566P1124) Hs.321022 N62953: 780 FLJ20094: hypothetical protein FLJ20094 Hs.29700 N95490 781 LOC51174: delta-tubulin Hs.270847 W33133 782 Homo sapiens mRNA; cDNA DKFZp434I1820 (from clone DKFZp434I1820); partial cds Hs.14235 N52394: 783 FANCA: Fanconi anemia, complementation group A Hs.284153 AA644129 784 P5-1: MHC class I region ORF Hs.1845 T58146 785 DNA2L: DNA2 (DNA replication helicase, yeast, homolog)-like Hs.194665 AA974495 KIAA0083 786 LOC51578: **adrenal gland protein AD-004 Hs.279586 AA150301 787 ESTs: Hs.326417 AA913304 788 CDKN2D: cyclin-dependent kinase inhibitor 2D (p19, inhibits CDK4) Hs.29656 R77517 p19-INK4D = Cyclin-dependent kinase 4 inhibitor D 789 FABP1: fatty acid binding protein 1, liver Hs.5241 AA682392 790 TERA: TERA protein Hs.180780 AA906997 791 ESTs: Hs.145383 AI253072 792 SLC7A5: solute carrier family 7 (cationic amino acid transporter, y+ system), member 5 Hs.184601 AA419176 793 AXL: AXL receptor tyrosine kinase Hs.83341 H15336 axl = ufo = tyrosine kinase receptor 794 LOC57190: selenoprotein N Hs.8518 AA284276 795 ESTs: Hs.99037 AA443948 796 STCH: stress 70 protein chaperone, microsome-associated, 60 kD Hs.288799 H85311 797 ESTs: Hs.88523 AA278591 Unknown UG Hs.88523 ESTs 798 ESD: **esterase D/formylglutathione hydrolase Hs.82193 AA250931 799 ESTs: Hs.122444 R31021 800 ESTs: Hs.283127 AI291262 801 KIAA0480: **KIAA0480 gene product Hs.92200 H91332 802 HP1-BP74: HP1-BP74 Hs.142442 AA598791 803 **ESTs,: Moderately similar to ALU1_HUMAN ALU SUBFAMILY J SEQUENCE CONTAMINATION WARNING ENTRY [H. sapiens] Hs.144662 AA987667 804 TTF2: transcription termination factor, RNA polymerase II Hs.142157 AI023603 805 ESTs: Hs.13740 T70541 806 DJ37E16.5: hypothetical protein dJ37E16.5 Hs.5790 AA400021 807 CDH24: cadherin-like 24 Hs.155912 AI732266 808 DJ465N24.2.1: **hypothetical protein dJ465N24.2.1 Hs.8084 AA932375 809 ESTs,: Weakly similar to S57447 HPBRII-7 protein [H. sapiens] Hs.16346 AA410490 810 Homo sapiens cDNA: FLJ23285 fis, clone HEP09071 Hs.90424 AI005038: 811 KRAS2: v-Ki-ras2 Kirsten rat sarcoma 2 viral oncogene homolog Hs.184050 N95249 812 FLJ20038: hypothetical protein FLJ20038 Hs.72071 H96090 813 ESTs,: Weakly similar to ALU4_HUMAN ALU SUBFAMILY SB2 SEQUENCE CONTAMINATION WARNING ENTRY [H. sapiens] Hs.28848 AA486607 814 H2AFN: H2A histone family, member N Hs.134999 AI095013 815 RERE: arginine-glutamic acid dipeptide (RE) repeats Hs.194369 AA490249 816 USP1: ubiquitin specific protease 1 Hs.35086 T55607 817 TIP47: cargo selection protein (mannose 6 phosphate receptor binding protein) Hs.140452 AA416787 818 KIAA0135: KIAA0135 protein Hs.79337 AA427740 KIAA0135 = related to pim-1 kinase. 819 ESTs: Hs.214410 T95273 820 PPP1R2: protein phosphatase 1, regulatory (inhibitor) subunit 2 Hs.267819 N52605 821 Homo sapiens cDNA: FLJ21210 fis, clone COL00479 Hs.325093 AA978323: 822 CSNK2A2: casein kinase 2, alpha prime polypeptide Hs.82201 AA054996 823 HSRTSBETA: rTS beta protein Hs.180433 N66132 824 FLJ13110: hypothetical protein FLJ13110 Hs.7358 AA431233 825 ESTs: Hs.238797 N30704 826 FYN: FYN oncogene related to SRC, FGR, YES Hs.169370 N35086 827 RBM8A: RNA binding motif protein 8A Hs.65648 AA448402 828 ESTs: Hs.21906 AA608546 829 ESTs: Hs.128081 AA971042 830 PP591: hypothetical protein PP591 Hs.118666 AA626336 831 N63866: 832 HM74: putative chemokine receptor; GTP-binding protein Hs.137555 R02739 833 MID1: midline 1 (Opitz/BBB syndrome) Hs.27695 AA598640 834 KIAA1586: KIAA1586 protein Hs.180663 AA938639 835 Homo sapiens clone CDABP0014 mRNA sequence Hs.92679 AA443139: 836 HSU79274: protein predicted by clone 23733 Hs.150555 AA451900 837 AOC3: amine oxidase, copper containing 3 (vascular adhesion protein 1) Hs.198241 AA036974 838 AA548037: 839 FLJ10154: hypothetical protein FLJ10154 Hs.179972 AA457133 840 THBS1: thrombospondin 1 Hs.87409 AA464532 841 DNAJB6: DnaJ (Hsp40) homolog, subfamily B, member 6 Hs.181195 AA431203 842 KIAA1547: KIAA1547 protein Hs.31305 AI216623 843 GATA2: GATA-binding protein 2 Hs.760 R32405 844 ESTs: Hs.176950 R82522 845 KIAA1018: KIAA1018 protein Hs.5400 AA156859 846 B4GALT1: **UDP-Gal:betaGlcNAc beta 1,4-galactosyltransferase, polypeptide 1 Hs.198248 AA043795 847 HMGCR: 3-hydroxy-3-methylglutaryl-Coenzyme A reductase Hs.11899 AA779417 848 ESTs,: Weakly similar to 1819485A CENP-E protein [H. sapiens] Hs.167652 H94466 849 ESTs: Hs.294088 AA971073 850 KIAA1637: coactivator independent of AF-2 (CIA); KIAA1637 protein Hs.288140 AA918007 851 HSPC196: hypothetical protein Hs.239938 H66023 852 DR1: down-regulator of transcription 1, TBP-binding (negative cofactor 2) Hs.16697 AA132007 853 CG1I: putative cyclin G1 interacting protein Hs.10028 AA486444 854 IGSF4: immunoglobulin superfamily, member 4 Hs.70337 AA487505 855 ESTs: Hs.179309 AA664350 856 HSPC163: HSPC163 protein Hs.108854 AA053139 857 FLJ12788: hypothetical protein FLJ12788 Hs.20242 AI061317 858 FEM1B: FEM-1 (C. elegans) homolog b Hs.6048 H82273 859 FXR1: fragile X mental retardation, autosomal homolog 1 Hs.82712 N62761 860 NCOA3: nuclear receptor coactivator 3 Hs.225977 AA156793 861 H2BFB: H2B histone family, member B Hs.180779 N33927 862 ESTs: Hs.23830 AA460601 863 CDK7: cyclin-dependent kinase 7 (homolog of Xenopus MO15 cdk-activating kinase) Hs.184298 AA031961 CAK = cdk7 = NRTALRE = sdk = CDK activating kinase 864 FLJ20259: hypothetical protein FLJ20259 Hs.9956 T55949 865 Homo sapiens cDNA FLJ20678 fis, clone KAIA4163 Hs.143601 T95823: 866 RPS19: ribosomal protein S19 Hs.298262 T72208 867 Homo sapiens mRNA; cDNA DKFZp434M0420 (from clone DKFZp434M0420) Hs.326048 AA443976: 868 TP53: tumor protein p53 (Li-Fraumeni syndrome) Hs.1846 R39356 p53 869 FBI1: HIV-1 inducer of short transcripts binding protein Hs.104640 R06252 870 GOT1: glutamic-oxaloacetic transaminase 1, soluble (aspartate aminotransferase 1) Hs.597 H22855 871 FLJ21434: hypothetical protein FLJ21434 Hs.298503 AA680129 872 DNMT2: DNA (cytosine-5-)-methyltransferase 2 Hs.97681 R95731 873 ESTs: Hs.55272 W02785 874 H2BFQ: H2B histone family, member Q Hs.2178 AA010223 875 NFIC: nuclear factor I/C (CCAAT-binding transcription factor) Hs.184771 N20996 876 NPTX1: neuronal pentraxin I Hs.84154 H22445 877 TLOC1: translocation protein 1 Hs.8146 AA450205 878 MGC5302: endoplasmic reticulum resident protein 58; hypothetical protein MGC5302 Hs.44970 N39195 879 ACTR2: ARP2 (actin-related protein 2, yeast) homolog Hs.42915 AA032090 880 AI287555: 881 ABCA7: ATP-binding cassette, sub-family A (ABC1), member 7 Hs.134514 AI668632 882 COL7A1: collagen, type VII, alpha 1 (epidermolysis bullosa, dystrophic, dominant and recessive) Hs.1640 AA598507 883 RFC2: replication factor C (activator 1) 2 (40 kD) Hs.139226 AA663472 884 FLJ22583: hypothetical protein FLJ22583 Hs.287700 AA135836 885 **ESTs,: Weakly similar to ORF2 [M. musculus] Hs.172208 AI820570 886 ESTs: Hs.21667 R15709 887 RBBP4: retinoblastoma-binding protein 4 Hs.16003 AA705035 888 Homo sapiens mRNA; cDNA DKFZp434J1027 (from clone DKFZp434J1027); partial cds Hs.22908 820166: 889 ESTs: Hs.166539 AI080987 890 NKTR: natural killer-tumor recognition sequence Hs.241493 AA279666 NK-tumor recognition protein = cyclophilin-related protein 891 MUC1: mucin 1, transmembrane Hs.89603 AA486365 892 AP4B1: adaptor-related protein complex 4, beta 1 subunit Hs.28298 AA481045 893 ESTs: Hs.94943 AA452165 894 MITF: microphthalmia-associated transcription factor Hs.166017 N66177 895 ESTs: Hs.183299 AA286914 Unknown UG Hs.183299 ESTs sc_id2032 896 BAG3: BCL2-associated athanogene 3 Hs.15259 AI269958 897 INSR: insulin receptor Hs.89695 AA001106 898 TRIP: TRAF interacting protein Hs.21254 AA186426 899 EST: Hs.307975 R22182 900 **Homo sapiens cDNA: FLJ23037 fis, clone LNG02036, highly similar to HSU68019 Homo sapiens mad protein homolog (hMAD-3) mRNA Hs.288261 W72201: 901 HLA-DNA: major histocompatibility complex, class II, DN alpha Hs.11135 AA702254 Major histocompatibility complex, class II, DN alpha 902 FLJ10392: **hypothetical protein FLJ10392 Hs.20887 AI261305 903 MPHOSPH1: **M-phase phosphoprotein 1 Hs.240 N63752 904 STAG1: stromal antigen 1 Hs.286148 R36160 905 USP1: ubiquitin specific protease 1 Hs.35086 AA970066 906 ESTs,: Moderately similar to ALU4_HUMAN ALU SUBFAMILY SB2 SEQUENCE CONTAMINATION WARNING ENTRY [H. sapiens] Hs.181315 AA448251 907 PA26: p53 regulated PA26 nuclear protein Hs.14125 AA447661 908 ESTs,: Weakly similar to zinc finger protein [H. sapiens] Hs.71243 N92478 909 SH3PX1: SH3 and PX domain-containing protein SH3PX1 Hs.7905 R69163 910 **Homo sapiens cDNA: FLJ22554 fis, clone HSI01092 Hs.93842 H58317: 911 RPS25: ribosomal protein S25 Hs.113029 AA779404 912 ESTs,: Weakly similar to A49134 Ig kappa chain V-I region [H. sapiens] Hs.5890 N34799 fra-2 = fos-related antigen 2 913 TXNRD1: thioredoxin reductase 1 Hs.13046 AA453335 Thioredoxin reductase 914 **ESTs: Hs.184378 N77828 915 GCSH: glycine cleavage system protein H (aminomethyl carrier) Hs.77631 R71327 916 Homo sapiens cDNA FLJ11904 fis, clone HEMBB1000048 Hs.285519 AA447098: 917 NCOA3: nuclear receptor coactivator 3 Hs.225977 H51992 AIB1 = Amplified in Breast Cancer = TRAM-1 = RAC3 = ACTR = CAGH16 = nucl 918 FLJ20159: hypothetical protein FLJ20159 Hs.288809 R33122 919 IL7R: interleukin 7 receptor Hs.237868 AA487121 920 RAB23: RAB23, member RAS oncogene family Hs.94769 AA134569 921 ESTs: Hs.132493 AA923168 922 ESTs: Hs.87507 AA236015 923 SHC1: SHC (Src homology 2 domain-containing) transforming protein 1 Hs.81972 R52960 924 KIAA1321: KIAA1321 protein Hs.24336 W37999 925 GLI: glioma-associated oncogene homolog (zinc finger protein) Hs.2693 AI373071 926 ESTs: Hs.183299 AA291137 Unknown UG Hs.183299 ESTs sc_id2032 927 GPRK6: G protein-coupled receptor kinase 6 Hs.76297 AA291284 928 ESTs: Hs.93704 AA702684 929 CAPS: calcyphosine Hs.26685 AA858390 930 Homo sapiens cDNA FLJ10976 fis, clone PLACE1001399 Hs.296323 R27711: 931 C6: complement component 6 Hs.1282 N59396 932 UBE2D3: ubiquitin-conjugating enzyme E2D 3 (homologous to yeast UBC4/5) Hs.118797 AA465196 933 DDX8: DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 8 (RNA helicase) Hs.171872 AA465387 RNA helicase (HRH1) 934 DKFZP434B168: DKFZP434B168 protein Hs.48604 N62684 935 FLJ10512: hypothetical protein FLJ10512 Hs.93581 T39933 936 Homo sapiens mRNA; cDNA DKFZp564F093 (from clone DKFZp564F093) Hs.18724 W87709: 937 F8A: coagulation factor VIII-associated (intronic transcript) Hs.83363 AA463924 938 HSU53209: transformer-2 alpha (htra-2 alpha) Hs.24937 AA465172 939 UBQLN2: ubiquilin 2 Hs.4552 R43580 940 EIF2C2: eukaryotic translation initiation factor 2C, 2 Hs.193053 N93082 941 Homo sapiens mRNA for FLJ00012 protein, partial cds Hs.21051 H17645: 942 KIAA0841: KIAA0841 protein Hs.7426 R20299 943 KCNAB2: potassium voltage-gated channel, shaker-related subfamily, beta member 2 Hs.298184 H14383 944 KIAA1637: coactivator independent of AF-2 (CIA); KIAA1637 protein Hs.288140 AA521358 945 ESTs: Hs.27379 H17455 946 FLJ11323: hypothetical protein FLJ11323 Hs.25625 R49707 947 SSP29: acidic protein rich in leucines Hs.84264 AA489201 948 ESTs: Hs.69280 AA486011 949 ADAMTS1: a disintegrin-like and metalloprotease (reprolysin type) with thrombospondin type 1 motif, 1 Hs.8230 AA057170 950 ESTs: Hs.43466 N23889 951 MLLT4: myeloid/lymphoid or mixed-lineage leukemia (trithorax (Drosophila) homolog); translocated to, 4 Hs.100469 AA010818 952 ESTs: Hs.271034 AA406581 953 LMNB1: lamin B1 Hs.89497 AA983462 954 Homo sapiens cDNA FLJ13547 fis, clone PLACE1007053 Hs.7984 AA629264: 955 PTMS: parathymosin Hs.171814 R10451 956 H2AFL: H2A histone family, member L Hs.28777 AI268551 957 FLJ21603: hypothetical protein FLJ21603 Hs.129691 R72794 958 FLJ13287: hypothetical protein FLJ13287 Hs.53263 AA621725 959 CXCR4: chemokine (C—X—C motif), receptor 4 (fusin) Hs.89414 AA479357 960 INSM1: insulinoma-associated 1 Hs.89584 R38640 961 FREQ: frequenin (Drosophila) homolog Hs.301760 H16821 962 LOC58486: transposon-derived Buster1 transposase-like protein Hs.25726 AA868020 963 SMARCD1: SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily d, member 1 Hs.79335 H91691 964 ESTs: Hs.242998 T96522 965 INADL: PDZ domain protein (Drosophila inaD-like) Hs.321197 AA005153 966 ESTs,: Weakly similar to putative p150 [H. sapiens] Hs.37751 AA436174 967 MGC5338: hypothetical protein MGC5338 Hs.99598 H50550 968 W85890: 969 NUCKS: similar to rat nuclear ubiquitous casein kinase 2 Hs.118064 AI053436 970 Homo sapiens clone 25110 mRNA sequence Hs.27262 H18031: 971 AI333214: 972 GAS41: glioma-amplified sequence-41 Hs.4029 T62072 973 LOC51170: retinal short-chain dehydrogenase/reductase retSDR2 Hs.12150 N79745 974 H2BFG: **H2B histone family, member G Hs.182137 R98472 975 ABCC1: **ATP-binding cassette, sub-family C (CFTR/MRP), member 1 Hs.89433 AA424804 976 EFNA1: ephrin-A1 Hs.1624 AA857015 977 Homo sapiens mRNA; cDNA DKFZp434A1014 (from clone DKFZp434A1014); partial cds Hs.278531 H00596: 978 PPP2CA: protein phosphatase 2 (formerly 2A), catalytic subunit, alpha isoform Hs.91773 AA599092 979 ESTs,: Weakly similar to unnamed protein product [H. sapiens] Hs.118338 W85843 980 Homo sapiens cDNA FLJ11643 fis, clone HEMBA1004366 Hs.111496 AA598803: 981 ESTs,: Moderately similar to ALUE_HUMAN !!!! ALU CLASS E WARNING ENTRY !!! [H. sapiens] Hs.125407 AA878944 982 ESTs,: Moderately similar to ALU1_HUMAN ALU SUBFAMILY J SEQUENCE CONTAMINATION WARNING ENTRY [H. sapiens] Hs.144662 AI191290 983 KIAA0916: KIAA0916 protein Hs.151411 R91388 984 CDC25A: cell division cycle 25A Hs.1634 R09062 985 PRIM2A: primase, polypeptide 2A (58 kD) Hs.74519 R61073 986 DSP: desmoplakin (DPI, DPII) Hs.74316 H90899 987 KIAA0101: KIAA0101 gene product Hs.81892 W68219 988 ESTs,: Weakly similar to putative p150 [H. sapiens] Hs.268026 AA411454 989 ESTs: Hs.18140 T97707 990 H2AFL: H2A histone family, member L Hs.28777 AA457566 991 Homo sapiens mRNA for KIAA1700 protein, partial cds Hs.20281 H00287: 992 STAG3: stromal antigen 3 Hs.20132 AA453028 993 ZNF207: zinc finger protein 207 Hs.62112 N59119 994 BMP6: bone morphogenetic protein 6 Hs.285671 AA424833 995 ESTs,: Moderately similar to sertolin [R. norvegicus] Hs.91192 H60690 996 LOC51064: **glutathione S-transferase subunit 13 homolog Hs.279952 W88497 997 NUCKS: similar to rat nuclear ubiquitous casein kinase 2 Hs.118064 AA927182 998 ESTs,: Weakly similar to T00370 hypothetical protein KIAA0659 [H. sapiens] Hs.131899 W93155 999 FLJ13057: hypothetical protein FLJ13057 similar to germ cell-less Hs.243122 R23254 1000 ESTs: Hs.144796 AI219737 1001 FLJ10511: hypothetical protein FLJ10511 Hs.106768 R25877 1002 DKFZP564A122: DKFZP564A122 protein Hs.187991 N31577 1003 ODF2: outer dense fibre of sperm tails 2 Hs.129055 AA400407 1004 AMY2A: amylase, alpha 2A; pancreatic Hs.278399 R64129 1005 **ESTs,: Weakly similar to plakophilin 2b [H. sapiens] Hs.12705 N91589 1006 CYP1B1: cytochrome P450, subfamily I (dioxin-inducible), polypeptide 1 (glaucoma 3, primary infantile) Hs.154654 AA029776 1007 CAPN7: calpain 7 Hs.7145 N46420 1008 FLJ20069: hypothetical protein FLJ20069 Hs.273294 AA229966 1009 FLJ10618: hypothetical protein FLJ10618 Hs.42484 AA478847 1010 KIAA1637: **coactivator independent of AF-2 (CIA); KIAA1637 protein Hs.288140 AA452531 1011 FLJ20004: **hypothetical protein FLJ20004 Hs.17311 AA487895 1012 FLJ12892: hypothetical protein FLJ12892 Hs.17731 AA670363 1013 PLU-1: putative DNA/chromatin binding motif Hs.143323 AA464869 1014 **ESTs: Hs.36828 AA418448 1015 KIAA0586: KIAA0586 gene product Hs.77724 AA905278 1016 MTHFD2: methylene tetrahydrofolate dehydrogenase (NAD+ dependent), methenyltetrahydrofolate cyclohydrolase Hs.154672 AA480994 1017 BRF1: **butyrate response factor 1 (EGF-response factor 1) Hs.85155 AA424743 1018 TFAP2A: transcription factor AP-2 alpha (activating enhancer-binding protein 2 alpha) Hs.18387 R38044 1019 VIL2: villin 2 (ezrin) Hs.155191 AA411440 1020 SDC1: syndecan 1 Hs.82109 AA074511 1021 RNTRE: related to the N terminus of tre Hs.278526 AA281057 1022 HSPC207: hypothetical protein Hs.75798 H99997 1023 FLJ22376: hypothetical protein FLJ22376 Hs.29341 AI199155 1024 RNF10: ring finger protein 10 Hs.5094 H73586 1025 PNN: pinin, desmosome associated protein Hs.44499 AA707321 1026 FLJ20516: hypothetical protein FLJ20516 Hs.70811 AA122393 1027 RPL13A: ribosomal protein L13a Hs.119122 AI254200 1028 H2BFB: H2B histone family, member B Hs.180779 AA885642 1029 OGT: O-linked N-acetylglucosamine (GlcNAc) transferase (UDP-N- acetylglucosamine:polypeptide-N-acetylglucosaminyl transferase) Hs.100293 R13317 1030 KIAA0155: KIAA0155 gene product Hs.173288 AA133684 1031 ILF2: interleukin enhancer binding factor 2, 45 kD Hs.75117 H95638 1032 Homo sapiens mRNA; cDNA DKFZp586I1518 (from clone DKFZp586I1518) Hs.21739 AA287917: 1033 PKNOX1: PBX/knotted 1 homeobox 1 Hs.158225 AI350546 1034 KMO: **kynurenine 3-monooxygenase (kynurenine 3-hydroxylase) Hs.107318 AA044326 1035 VCAM1: vascular cell adhesion molecule 1 Hs.109225 H16591 CD106 = VCAM-1 1036 N54811: 1037 KIAA0618: KIAA0618 gene product Hs.295112 H81940 1038 MAFG: v-maf musculoaponeurotic fibrosarcoma (avian) oncogene family, protein G Hs.252229 N21609 MafG = basic-leucine zipper transcription factor 1039 MATN2: matrilin 2 Hs.19368 AA071473 1040 HOXB4: homeo box B4 Hs.126666 AA918749 1041 FLJ10466: hypothetical protein FLJ10466 Hs.121073 AA453607 1042 FLJ22557: hypothetical protein FLJ22557 Hs.106101 AA127879 1043 EST: Hs.149260 AI247680 1044 KIAA0677: KIAA0677 gene product Hs.155983 AA026751 1045 EST: Hs.104123 AA197344 1046 UCP4: uncoupling protein 4 Hs.40510 H94680 1047 EST: Hs.144224 N93807 1048 GATA2: GATA-binding protein 2 Hs.760 H00625 GATA-binding protein 2 1049 ESTs: Hs.14743 H61082 1050 EST: Hs.116174 AA626786 1051 ITGB3: integrin, beta 3 (platelet glycoprotein IIIa, antigen CD61) Hs.87149 AA666269 1052 FLJ23399: hypothetical protein FLJ23399 Hs.299883 R19895 1053 ESTs: Hs.21734 N72976 1054 FLJ20425: hypothetical protein FLJ20425 Hs.71040 AA424566 1055 CUL4A: cullin 4A Hs.183874 AA598836 1056 PTP4A1: protein tyrosine phosphatase type IVA, member 1 Hs.227777 R61007 protein tyrosine phosphatase PTPCAAX1 (hPTPCAAX1) 1057 ESTs: Hs.7913 N35592 1058 GRO1: GRO1 oncogene (melanoma growth stimulating activity, alpha) Hs.789 W46900 1059 ESTs,: Moderately similar to NRD2 convertase [H. sapiens] Hs.309734 H78796 1060 FLJ10826: hypothetical protein FLJ10826 Hs.24809 AA486738 1061 TOM34: translocase of outer mitochondrial membrane 34 Hs.76927 AA457118 1062 H2AFL: H2A histone family, member L Hs.28777 AA452933 1063 D10S170: **DNA segment, single copy, probe pH4 (transforming sequence, thyroid- 1, Hs.315591 N35493 1064 SCYA2: small inducible cytokine A2 (monocyte chemotactic protein 1, homologous to mouse Sig-je) Hs.303649 T77816 MCP-1 = MCAF = small inducible cytokine A2 = JE = chemokine 1065 FLJ10688: hypothetical protein FLJ10688 Hs.118793 AA465358 1066 PTD017: PTD017 protein Hs.274417 AA160498 1067 KIAA0026: MORF-related gene X Hs.173714 AA676604 1068 BMP2: bone morphogenetic protein 2 Hs.73853 AA489383 1069 MNT: MAX binding protein Hs.25497 AA455508 1070 KIAA1170: KIAA1170 protein Hs.268044 H80507 1071 CRYBA1: crystallin, beta A1 Hs.46275 AA487614 1072 KATNA1: katanin p60 (ATPase-containing) subunit A 1 Hs.289099 AA609740 1073 Homo sapiens cDNA FLJ20796 fis, clone COL00301 Hs.113994 N53458: 1074 CEP4: Cdc42 effector protein 4; binder of Rho GTPases 4 Hs.3903 W32509 1075 ESTs: Hs.117261 AA682521 1076 CYP1B1: cytochrome P450, subfamily I (dioxin-inducible), polypeptide 1 (glaucoma 3, primary infantile) Hs.154654 AA040872 1077 ALTE: Ac-like transposable element Hs.9933 AA630498 1078 RAD51: RAD51 (S. cerevisiae) homolog (E coli RecA homolog) Hs.23044 AA873056 1079 MAN1A2: mannosidase, alpha, class 1A, member 2 Hs.239114 R78501 1080 H53763: 1081 MET: met proto-oncogene (hepatocyte growth factor receptor) Hs.285754 AA410591 1082 DYRK1A: dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A Hs.75842 AA676749 1083 ARHGAP8: **Rho GTPase activating protein 8 Hs.102336 AA037410 1084 LMO4: LIM domain only 4 Hs.3844 H27986 1085 ADCY6: adenylate cyclase 6 Hs.12373 AA148044 1086 EST: Hs.135448 AI078552 1087 NCOA3: nuclear receptor coactivator 3 Hs.225977 W46433 1088 DNAJB4: DnaJ (Hsp40) homolog, subfamily B, member 4 Hs.41693 AA081471 1089 NAB1: NGFI-A binding protein 1 (ERG1 binding protein 1) Hs.107474 AA486027 1090 ESTs,: Weakly similar to T08663 hypothetical protein DKFZp547G0910.1 [H. sapiens] Hs.172084 N63646 1091 KIAA0735: KIAA0735 gene product; synaptic vesicle protein 2B homolog Hs.8071 R56082 1092 GNB1: guanine nucleotide binding protein (G protein), beta polypeptide 1 Hs.215595 AA487912 1093 Homo sapiens mRNA for KIAA1716 protein, partial cds Hs.21446 R49763: 1094 KINESIN: HEAVY CHAIN 1095 CCND1: cyclin D1 (PRAD1: parathyroid adenomatosis 1) Hs.82932 AA487486 Cyclin D1 = BCL1 = PRAD1 = Translocated in mantle cell leukemia 1096 ESTs: Hs.106129 R56716 1097 AA431931: 1098 PSEN1: presenilin 1 (Alzheimer disease 3) Hs.3260 AA403083 1099 ESTs: Hs.193804 AA010918 1100 DKFZp762P2111: hypothetical protein DKFZp762P2111 Hs.14217 AA429586 1101 KIAA1350: KIAA1350 protein Hs.101799 W37627 1102 FLJ20847: hypothetical protein FLJ20847 Hs.13479 H16996 1103 HDCMA18P: HDCMA18P protein Hs.278635 N64387 1104 FLJ12890: hypothetical protein FLJ12890 Hs.43299 N62475 1105 ESTs: Hs.127453 AA973625 1106 BAIAP2: BAI1-associated protein 2 Hs.7936 R60328 1107 ESTs: Hs.317584 AA191424 1108 DKFZP434J046: DKFZP434J046 protein Hs.116244 AI024401 1109 ESTs: Hs.114055 AA701352 1110 ESTs: Hs.44380 N93122 1111 ESTs: Hs.20142 AA625570 1112 UBL3: ubiquitin-like 3 Hs.173091 T82438 1113 H2AFL: H2A histone family, member L Hs.28777 N50797 1114 SUCLG2: **succinate-CoA ligase, GDP-forming, beta subunit Hs.247309 N68557 1115 ZWINT: ZW10 interactor Hs.42650 AA706968 1116 FLJ10583: hypothetical protein FLJ10583 Hs.105633 R00425 1117 FLJ20552: hypothetical protein FLJ20552 Hs.69554 AA463982 1118 FADD: Fas (TNFRSF6)-associated via death domain Hs.86131 AA430751 FADD = MORT 1119 SFRS7: splicing factor, arginine/serine-rich 7 (35 kD) Hs.184167 AA418813 1120 RAD54L: RAD54 (S. cerevisiae)-like Hs.66718 AI372035 1121 MYLE: MYLE protein Hs.11902 T68845 1122 LOC51334: mesenchymal stem cell protein DSC54 Hs.157461 R63841 1123 PRIM2A: primase, polypeptide 2A (58 kD) Hs.74519 AA434404 1124 KIAA0056: KIAA0056 protein Hs.13421 AA430545 1125 ESTs,: Moderately similar to ALU7_HUMAN ALU SUBFAMILY SQ SEQUENCE CONTAMINATION WARNING ENTRY [H. sapiens] Hs.82590 N53024 1126 ESTs: Hs.117269 AA705050 1127 NSAP1: NS1-associated protein 1 Hs.155489 AA186327 1128 CEACAM5: carcinoembryonic antigen-related cell adhesion molecule 5 Hs.220529 AA130547 1129 FLJ11021: hypothetical protein FLJ11021 similar to splicing factor, arginine/serine- rich 4 Hs.81648 AA291183 Unknown UG Hs.202583 ESTs, Weakly similar to arginine-rich 1130 FOSL1: FOS-like antigen-1 Hs.283565 T82817 fra-1 = fos-related antigen 1 1131 U3-55K: U3 snoRNP-associated 55-kDa protein Hs.153768 AA465355 1132 DNAJC6: DnaJ (Hsp40) homolog, subfamily B, member 6 Hs.44896 AA455940 1133 KIAA1382: amino acid transporter 2 Hs.298275 R27255 Similar to transporter protein 1134 PCAF: p300/CBP-associated factor Hs.199061 N74637 P/CAF = p300/CBP-associated factor 1135 ESTs: Hs.130460 AA927252 1136 ESTs: Hs.112570 AI014667 1137 FLJ10209: hypothetical protein FLJ10209 Hs.260150 AA454626 1138 ESTs: Hs.99014 AA485679 1139 ESTs: Hs.99621 AA464707 1140 Homo sapiens cDNA FLJ11904 fis, clone HEMBB1000048 Hs.285519 N74617: 1141 AA928536: 1142 SQSTM1: **sequestosome 1 Hs.182248 AA931964 1143 **Homo sapiens cDNA FLJ13700 fis, clone PLACE2000216, highly similar to SPECTRIN BETA CHAIN, BRAIN Hs.324648 AA018591: 1144 SLC22A3: solute carrier family 22 (extraneuronal monoamine transporter), member 3 Hs.81086 AA460012 1145 FLJ22557: hypothetical protein FLJ22557 Hs.106101 H00595 1146 FLJ20539: hypothetical protein FLJ20539 Hs.118552 R36152 1147 AA991624: 1148 TRAP150: thyroid hormone receptor-associated protein, 150 kDa subunit Hs.108319 W85832 1149 ESTs: Hs.221847 R91557 1150 TCFL1: transcription factor-like 1 Hs.2430 AA443950 1151 ESTs,: Highly similar to oxytocinase splice variant 1 [H. sapiens] Hs.203271 AA487918 1152 PLAB: prostate differentiation factor Hs.296638 AA450062 1153 RBM14: RNA binding motif protein 14 Hs.11170 AA417283 1154 EGFL5: EGF-like-domain, multiple 5 Hs.5599 W67981 1155 H2AFO: H2A histone family, member O Hs.795 AA047260 1156 ESTs,: Weakly similar to A46661 leukotriene B4 omega-hydroxylase [H. sapiens] Hs.169001 N45556 1157 W78784: 1158 TOP3A: topoisomerase (DNA) III alpha Hs.91175 N21546 1159 W73732: Host cell factor-1 = VP16 transactivator interacting protein 1160 CYP1B1: cytochrome P450, subfamily I (dioxin-inducible), polypeptide 1 (glaucoma 3, primary infantile) Hs.154654 AA448157 Cytochrome P450 IB1 (dioxin-inducible) 1161 ESTs: Hs.135276 AI092102 1162 RHEB2: Ras homolog enriched in brain 2 Hs.279903 AA482117 1163 ESTs,: Highly similar to EF-9 [M. musculus] Hs.8366 H94467 1164 POLA: polymerase (DNA directed), alpha Hs.267289 AA707650 1165 KIAA1008: KIAA1008 protein Hs.323346 AA863115 1166 PIK3CD: phosphoinositide-3-kinase, catalytic, delta polypeptide Hs.162808 AA281652 1167 T53625: 1168 **Homo sapiens mRNA; cDNA DKFZp434A1114 (from clone DKFZp434A1114) Hs.326292 AA417274: 1169 ESTs: Hs.26744 H16988 1170 FLJ13912: hypothetical protein FLJ13912 Hs.47125 W74133 1171 Homo sapiens mRNA; cDNA DKFZp762B195 (from clone DKFZp762B195) Hs.284158 AA625574: 1172 SSA2: Sjogren syndrome antigen A2 (60 kD, ribonucleoprotein autoantigen SS-A/Ro) Hs.554 AA010351 1173 BK1048E9.5: hypothetical protein bK1048E9.5 Hs.6657 N68512 1174 TOP1: topoisomerase (DNA) I Hs.317 AA232856 Topoisomerase I 1175 ESTs: Hs.15386 H18472 1176 KPNB1: karyopherin (importin) beta 1 Hs.180446 AA121732 1177 MGC861: hypothetical protein MGC861 Hs.208912 N69694 1178 PMS2L8: **postmeiotic segregation increased 2-like 8 Hs.323954 T62577 1179 TSC22: **transforming growth factor beta-stimulated protein TSC-22 Hs.114360 R16390 1180 C8ORF1: chromosome 8 open reading frame 1 Hs.40539 AA278836 1181 ESTs: Hs.129165 AA989211 1182 DMTF: cyclin D binding Myb-like transcription factor 1 Hs.5671 AA129860 1183 CDC7L1: CDC7 (cell division cycle 7, S. cerevisiae, homolog)-like 1 Hs.28853 N62245 Cdc7-related kinase 1184 LOC51700: cytochrome b5 reductase b5R.2 Hs.22142 AA425316 1185 FLNA: filamin A, alpha (actin-binding protein-280) Hs.195464 AA598978 1186 FLJ20257: hypothetical protein FLJ20257 Hs.178011 H78675 1187 Homo sapiens cDNA FLJ13604 fis, clone PLACE1010401 Hs.23193 AA406599: 1188 ESTs: Hs.205227 R73480 1189 SCYB14: small inducible cytokine subfamily B (Cys-X-Cys), member 14 (BRAK) Hs.24395 AA953842 1190 MAPK8IP2: **mitogen-activated protein kinase 8 interacting protein 2 Hs.80545 AA418293 1191 ZNF42: zinc finger protein 42 (myeloid-specific retinoic acid-responsive) Hs.169832 AA932642 1192 ESTs: Hs.127054 AA862450 1193 NUDT4: nudix (nucleoside diphosphate linked moiety X)-type motif 4 Hs.92381 AA425630 1194 Homo sapiens cDNA FLJ10632 fis, clone NT2RP2005637 Hs.202596 H82421: 1195 LOC51042: zinc finger protein Hs.102419 AA033532 1196 NUMA1: nuclear mitotic apparatus protein 1 Hs.301512 AA679293 1197 ESTs,: Highly similar to A56429 I-kappa-B-related protein [H. sapiens] Hs.144614 AA293771 1198 ESTs: Hs.127703 AA947258 1199 Homo sapiens cDNA FLJ14214 fis, clone NT2RP3003576 Hs.321236 AA903913: 1200 NFKBIA: nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha Hs.81328 W55872 IkB alpha 1201 ESTs: Hs.120029 AA707598 1202 ESTs,: Moderately similar to A Chain A, Human Glucosamine-6-Phosphate Deaminase Isomerase At 1.75 A [H. sapiens] Hs.21398 AA172012 1203 NFIA: nuclear factor I/A Hs.173933 AI912047 1204 RECQL4: RecQ protein-like 4 Hs.31442 AA620446 1205 **ESTs,: Weakly similar to ALU1_HUMAN ALU SUBFAMILY J SEQUENCE CONTAMINATION WARNING ENTRY [H. sapiens] Hs.318894 R96212 1206 Homo sapiens cDNA: FLJ21686 fis, clone COL09379 Hs.20787 R11371: 1207 LOC57168: similar to aspartate beta hydroxylase (ASPH) Hs.184390 H17272 1208 ESTs: Hs.26096 R54109 1209 Homo sapiens OSBP-related protein 6 mRNA, complete cds Hs.318775 AA680281: 1210 APACD: ATP binding protein associated with cell differentiation Hs.153884 N80741 1211 VIM: **vimentin Hs.297753 AI668662 1212 Homo sapiens cDNA FLJ13618 fis, clone PLACE1010925 Hs.17448 AA427980: 1213 NR3C1: nuclear receptor subfamily 3, group C, member 1 Hs.75772 N30428 Glucocorticoid receptor 1214 Homo sapiens cDNA: FLJ21814 fis, clone HEP01068 Hs.289008 R12808: 1215 BRD7: bromodomain-containing 7 Hs.279762 AA488428 1216 MAP3K8: **mitogen-activated protein kinase kinase kinase 8 Hs.248 W42450 1217 ESTs: Hs.23213 H29336 1218 ESTs: Hs.122444 AA939019 1219 TUSP: tubby super-family protein Hs.102237 H78234 1220 KIAA1117: KIAA1117 protein Hs.278398 H01516 1221 Human: clone 137308 mRNA, partial cds Hs.322149 H91303 1222 ESTs: Hs.130214 AA456631 1223 RAB3A: RAB3A, member RAS oncogene family Hs.27744 H14230 1224 AA598795: Protein phosphatase 2 (formerly 2A), regulatory subunit B (P 1225 H2BFC: H2B histone family, member C Hs.137594 AI340654 1226 CFLAR: CASP8 and FADD-like apoptosis regulator Hs.195175 N94588 1227 CD24: CD24 antigen (small cell lung carcinoma cluster 4 antigen) Hs.286124 H59915 1228 EST: Hs.48532 N62402 1229 CCRK: cell cycle related kinase Hs.26322 H17616 1230 HECH: heterochromatin-like protein 1 Hs.278554 AI139106 1231 DKFZp547O146: hypothetical protein DKFZp547O146 Hs.91246 T80848 1232 ESTs: Hs.71574 AA135328 1233 HLXB9: homeo box HB9 Hs.37035 AI459915 1234 AA600222: 1235 SPINK5: serine protease inhibitor, Kazal type, 5 Hs.5476 W92134 1236 RNUT1: RNA, U transporter 1 Hs.21577 AA447799 1237 Homo sapiens cDNA: FLJ23013 fis, clone LNG00740 Hs.13075 AA464543: 1238 KIAA0063: KIAA0063 gene product Hs.3094 T82263 1239 DYRK2: dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 2 Hs.173135 R63622 1240 R94947: 1241 Homo sapiens cDNA FLJ14337 fis, clone PLACE4000494 Hs.180187 AA004903: 1242 FLJ20624: hypothetical protein FLJ20624 Hs.52256 AA431909 1243 ESTs: Hs.43838 R38261 1244 FLJ23053: hypothetical protein FLJ23053 Hs.94037 R25654 1245 MGC11266: hypothetical protein MGC11266 Hs.293943 AA400456 1246 ESTs,: Moderately similar to ALU8_HUMAN ALU SUBFAMILY SX SEQUENCE CONTAMINATION WARNING ENTRY [H. sapiens] Hs.34174 AA126603 1247 PLAUR: plasminogen activator, urokinase receptor Hs.179657 AA147962 1248 TSG101: tumor susceptibility gene 101 Hs.118910 AA670215 1249 HCNGP: transcriptional regulator protein Hs.27299 AA457232 1250 KIAA0978: KIAA0978 protein Hs.3686 AA857017 1251 ESTs: Hs.61708 AA033867 1252 ESTs: Hs.120734 AA827482 1253 ESTs: Hs.5909 AA972654 1254 CDH24: cadherin-like 24 Hs.155912 AI668564 1255 CCND1: cyclin D1 (PRAD1: parathyroid adenomatosis 1) Hs.82932 T77237 1256 ESTs: Hs.43148 AA284775 1257 ESTs: Hs.222566 T50982 1258 ESTs: Hs.194125 N52822 1259 EST: Hs.154621 AI138644 1260 MAN1A2: mannosidase, alpha, class 1A, member 2 Hs.239114 R22905 1261 MAN2A2: mannosidase, alpha, class 2A, member 2 Hs.295605 AA454175 1262 Human DNA sequence from clone 967N21 on chromosome 20p12.3-13. Contains the CHGB gene for chromogranin B (secretogranin 1, SCG1), a pseudogene similar to part of KIAA0172, the gene for a novel protein Hs.88959 W94690 1263 ESTs,: Highly similar to CIKG_HUMAN VOLTAGE-GATED POTASSIUM CHANNEL PROTEIN KV3.4 [H. sapiens] Hs.106486 H11376 1264 Homo sapiens HT023 mRNA, complete cds Hs.237225 AA169496: 1265 FLJ10339: **hypothetical protein FLJ10339 Hs.203963 H72354 1266 N66278: 1267 ESTs: Hs.6195 AA454745 1268 KIAA1404: KIAA1404 protein Hs.200317 W72798 1269 PMAIP1: phorbol-12-myristate-13-acetate-induced protein 1 Hs.96 AA458838 APR = immediate-early-response gene = ATL-derived PMA-responsive 1270 G3BP: Ras-GTPase-activating protein SH3-domain-binding protein Hs.220689 AA598628 1271 Homo sapiens cDNA: FLJ22807 fis, clone KAIA2887 Hs.261734 R26854: 1272 Homo sapiens, clone IMAGE: 3535294, mRNA, partial cds Hs.80449 T57359: 1273 CDC16: CDC16 (cell division cycle 16, S. cerevisiae, homolog) Hs.1592 AA410559 1274 FGA: **fibrinogen, A alpha polypeptide Hs.90765 AA026626 1275 ESTs: Hs.33446 N53560 1276 Homo sapiens cDNA FLJ14175 fis, clone NT2RP2002979 Hs.288613 AA054704: 1277 ESTs: Hs.44243 AA011390 1278 Homo sapiens mRNA full length insert cDNA clone EUROIMAGE 42408 Hs.284123 R61732: 1279 ESTs: Hs.53455 AA454165 1280 FLJ11264: hypothetical protein FLJ11264 Hs.11260 AI219094 1281 MBD4: methyl-CpG binding domain protein 4 Hs.35947 AA010492 1282 FLJ11305: hypothetical protein FLJ11305 Hs.7049 N94612 1283 Homo sapiens, Similar to CG5057 gene product, clone MGC: 5309, mRNA, complete cds Hs.13885 AA460004: 1284 ARHB: ras homolog gene family, member B Hs.204354 H88963 1285 ITPR3: inositol 1,4,5-triphosphate receptor, type 3 Hs.77515 AA865667 1286 HMG20B: high-mobility group 20B Hs.32317 AA775743 1287 ESTs: Hs.146276 AI214204 1288 PTPN9: protein tyrosine phosphatase, non-receptor type 9 Hs.147663 AA434420 1289 Homo sapiens clone FLB9213 PRO2474 mRNA, complete cds Hs.21321 AA486770: 1290 H21107: 1291 HSPC157: HSPC157 protein Hs.279842 N20480 1292 Homo sapiens mRNA; cDNA DKFZp564O2363 (from clone DKFZp564O2363) Hs.321403 AA406332: 1293 ESTs: Hs.150623 AA693532 1294 EST: Hs.188697 AA199733 1295 CLECSF2: C-type (calcium dependent, carbohydrate-recognition domain) lectin, superfamily member 2 (activation-induced) Hs.85201 H11732 AICL = activation- induced C-type lectin 1296 ITPR1: inositol 1,4,5-triphosphate receptor, type 1 Hs.198443 AA035450 1297 CHML: choroideremia-like (Rab escort protein 2) Hs.170129 R91881 1298 CDC42: cell division cycle 42 (GTP-binding protein, 25 kD) Hs.146409 AA668681 1299 FKBP5: **FK506-binding protein 5 Hs.7557 AA872767

All publications and patent applications mentioned in the specification are indicative of the level of those skilled in the art to which this invention pertains. All publications and patent applications are herein incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference. The mere mentioning of the publications and patent applications does not necessarily constitute an admission that they are prior art to the instant application.

Although the foregoing invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, it will be obvious that certain changes and modifications may be practiced within the scope of the appended claims. 

What is claimed is:
 1. A method of classifying cancer comprising determining in a cancer sample from a patient the status of a panel of genes comprising at least two cell-cycle genes, wherein an abnormal cell-cycle gene status indicates a poor prognosis.
 2. An in vitro diagnostic method comprising determining the status of a panel of genes comprising at least two cell-cycle genes in a sample obtained from a patient.
 3. The method of claim 2, further comprising determining at least one clinical parameter for the cancer patient.
 4. The method of claim 2, wherein said cell-cycle genes are chosen from the genes listed in Table 1 or in any of Panels A through G.
 5. The method of claim 3, wherein a recurrence-associated clinical parameter or an abnormal cell-cycle gene status indicates a poor prognosis.
 6. The method of claim 1 further comprising determining the status of PTEN in a sample obtained from the patient, wherein either of abnormal cell-cycle gene status or abnormal PTEN status indicates a poor prognosis.
 7. The method of claim 1, wherein said cancer is prostate cancer.
 8. The method of claim 1, wherein said cancer is bladder cancer.
 9. The method of claim 1, wherein said cancer is brain cancer.
 10. The method of claim 1, wherein said cancer is lung cancer.
 11. The method of claim 1, wherein said panel comprises at least 15 cell-cycle genes.
 12. The method of claim 11, wherein said panel comprises at least 15 genes chosen from the group consisting of the genes in Table 1 or in any of Panels A through G.
 13. The method of claim 11, wherein said 15 genes comprise at least 75% of the genes in the panel.
 14. The method of claim 11, wherein said panel comprises the genes in Panel F.
 15. A method of treating a prostate cancer patient comprising determining at least one clinical parameter for said patient, determining the status of a plurality of cell-cycle genes in a sample obtained from the patient, and (a) recommending, prescribing or initiating active treatment if said patient has at least one risk-associated clinical parameter and an elevated status for said plurality of cell-cycle genes, or (b) recommending, prescribing or initiating watchful waiting if said patient has neither a risk-associated clinical parameter nor an elevated status for said plurality of cell-cycle genes.
 16. A computer-implemented method of determining whether a patient has an increased likelihood of cancer recurrence comprising: (a) accessing information on a patient's PTEN status or clinical parameters and CCG status stored in a computer-readable medium; (b) querying this information to determine at least one of whether the patient has a low/negative PTEN status or whether the patient has a recurrence-associated clinical parameter; (c) querying this information to determine whether a sample obtained from the patient shows increased expression of at least one CCG; and (d) outputting an indication that the patient has an increased likelihood of recurrence if the patient has a low/negative PTEN status or a recurrence-associated clinical parameter and the sample shows increased expression of at least one CCG.
 17. A kit comprising a plurality of oligonucleotides for determining the expression level of a panel of genes, where said panel comprises at least 75% cell-cycle genes.
 18. A system for determining whether a patient has an increased likelihood of recurrence, comprising: a sample analysis device for determining, in a prostate cancer sample, the expression of a panel of genes having at least two cell-cycle genes chosen from the group consisting of the genes in Table 1, and Panels A through G; and a computer processor programmed to execute the method of claim
 16. 